Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Application of artificial intelligence in modeling of nitrate removal process using zero-valent iron nanoparticles-loaded carboxymethyl cellulose

This study explores nitrate reduction in aqueous solutions using carboxymethyl cellulose loaded with zero-valent iron nanoparticles (Fe0-CMC). The structures of this nano-composite were characterized using various techniques. Based on the characterization results, the specific surface area of Fe0-CMC measured by the Brunauer-Emmett-Teller analysis were 39.6 m2/g. In addition, Scanning Electron Microscopy images displayed that spherical nano zero-valent iron particles (nZVI) with an average particle diameter of 80 nm are surrounded by carboxymethyl cellulose and no noticeable aggregates were detected. Batch experiments assessed Fe0-CMC's effectiveness in nitrate removal under diverse conditions including different adsorbent dosages (Cs, 2-10 mg/L), contact time (t, 10-1440 min), initial pH (pHi, 2-10), temperature (T, 10-55 °C), and initial concentration of nitrate (C0, 10-500 mg/L). Results indicated decreased removal with higher initial pHi and C0, while increased Cs and T enhanced removal. The study of nitrate removal mechanism by Fe0-CMC revealed that the redox reaction between immobilized nZVI on the CMC surface and nitrate ions was responsible for nitrate removal, and the main product of this reaction was ammonium, which was subsequently completely removed by the synthesized nanocomposite. In addition, a stable deviation quantum particle swarm optimization algorithm (SD-QPSO) and a least square error method were employed to train the ANFIS parameters. To demonstrate model performance, a quadratic polynomial function was proposed to display the performance of the SD-QPSO algorithm in which the constant parameters were optimized through the SD-QPSO algorithm. Sensitivity analysis was conducted on the proposed quadratic polynomial function by adding a constant deviation and removing each input using two different strategies. According to the sensitivity analysis, the predicted removal efficiency was most sensitive to changes in pHi, followed by Cs, T, C0, and t. The obtained results underscore the potential of the ANFIS model (R2 = 0.99803, RMSE = 0.9888), and polynomial function (R2 = 0.998256, RMSE = 1.7532) as accurate and efficient alternatives to time-consuming laboratory measurements for assessing nitrate removal efficiency. These models can offer rapid insights and predictions regarding the impact of various factors on the process, saving both time and resources.

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, review studies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determination coefficients (R, R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.

Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models

Arsenic contamination in the groundwater occurs in various parts of the world due to anthropogenic and natural sources, adversely affecting human health and ecosystems. The current study intends to examine the groundwater hydrogeochemistry containing elevated arsenic (As), predict As levels in groundwater, and determine the aptness of groundwater for drinking in the Vehari district, Pakistan. Four hundred groundwater samples from the study region were collected for physiochemical analysis. As levels in groundwater samples ranged from 0.1 to 52 μg/L, with an average of 11.64 μg/L, (43.5%), groundwater samples exceeded the WHO 2022 recommended limit of 10 μg/L for drinking purposes. Ion-exchange processes and the adsorption of ions significantly impacted the concentration of As. The HCO3- and Na+ are the dominant ions in the study area, and the water types of samples were CaHCO3, mixed CaMgCl, and CaCl, demonstrating that rock-water contact significantly impacts hydrochemical behavior. The geochemical modeling indicated negative saturation indices with calcium carbonate and other salt minerals, encompassing aragonite, calcite, dolomite, and halite. The dissolution mechanism suggested that these minerals might have implications for the mobilization of As in groundwater. A combination of human-induced and natural sources of contamination was unveiled through principal component analysis (PCA). Artificial neural networks (ANN), random forest (RF), and logistic regression (LR) were used to predict As in the groundwater. The data have been divided into two parts for statistical analysis: 20% for testing and 80% for training. The most significant input variables for As prediction was determined using Chi-squared analysis. The receiver operating characteristic area under the curve and confusion matrix were used to evaluate the models; the RF, ANN, and LR accuracies were 0.89, 0.85, and 0.76. The permutation feature and mean decrease in impurity determine ten parameters that influence groundwater arsenic in the study region, including F-, Fe2+, K+, Mg2+, Ca2+, Cl-, SO42-, NO3-, HCO3-, and Na+. The present study shows RF is the best model for predicting groundwater As contamination in the research area. The water quality index showed that 161 samples represent poor water, and 121 samples are unsuitable for drinking. Establishing effective strategies and regulatory measures is imperative in Vehari to ensure the sustainability of groundwater resources.

Modeling, quality assessment, and Sobol sensitivity of water resources and distribution system in Shiraz: A probabilistic human health risk assessment

Given water's vital role in supporting life and ecosystems, global climate change and human activities have significantly diminished its availability and quality. This study explores the health risks of drinking water consumption in the shiraz county water resources and distribution system. The result showed that the water was slightly alkaline. However, the average pH values during the study were within the permissible range. The area's abundance of total hardness and calcium was due to the high concentration of minerals in rocks and soils. The nitrate and fluoride concentrations in drinking groundwater varied from 0.02 to 116.70 mg/L and 0.10-1.85 mg/L, respectively. Although the water quality index indicated that 52.63, 45.03, and 20.3 percent of samples were of excellent, good, and poor quality in 2020, those percentages obtained 46.05, 52.09, and 14.0 percent in 2021. The regression values of training, testing, validation, and the proposed artificial neural network model were 0.93, 0.92, 0.85, and 0.92. The maximum levels of hazard quotient of nitrate and fluoride (except for adults) were higher than 1 in all age groups, indicating a high non-carcinogenic risk by exposure to nitrate. Furthermore, according to the Monte Carlo simulation, the 95th percentile hazard index in all groups was more than 1. Children and infants were more inclined towards risk than teens and adults based on the intake of nitrate and fluoride from drinking water. The Sobol sensitivity reflected that the nitrate concentration and ingestion rate are vital parameters that influence the outcome of the oral exposure model for all age groups. The interaction of ingestion rate with a concentration of nitrate and fluoride is an important parameter affecting the health risk assessment. In conclusion, these findings suggest that precise measures can reduce health risks and guarantee safe drinking water for residents of Shiraz County.

Effective removal of nitrate in water by continuous-flow electro-dialysis ion exchange membrane bioreactor (CF-EDIMB): Performance optimization and microbial analysis

The use of nitrogen fertilizer has been causing nitrate pollution in groundwater, and there is an urgent need for efficient approach to remove nitrate from groundwater. In our job, a novel continuous-flow electrodialysis ion exchange membrane bioreactor system (CF-EDIMB) was set up to remove nitrate (NO3-) from water for the first time. Nitrate removal was positively dependent on water chamber HRT and voltage; voltage had significant effect on the water chamber effluent pH; acetate utilization efficiency was closely correlated with acetate dosage. The optimal conditions forecasted through response surface method (RSM) were given as follows: water chamber HRT was 20 h, biological chamber HRT was 24 h, voltage was 6.65 V and acetate dosage was 454.99 mg/L, dedicating to nitrate removal of 81.90% (83.70% in prediction), water chamber effluent pH of 7.10 (7.00 in prediction) and acetate utilization efficiency of 92.87% (96.51% in prediction). Meanwhile, microorganisms are crucial for nitrate removal, and the microbial community was not sensitive to the variation of acetate dosage. The microbial analysis results indicated that when CF-EDIMB system was operated for 20 d, the sulfate-reducing bacteria Sediminibacterium appeared in the biological chamber, and the effluent sulfate concentration of biological chamber was decreased. During the whole operation, Thauera was the dominant genus. Denitrifying functional genes nirS presented a better expression than the gene narG, and there was no accumulation of nitrite.

Nitrates in Turkish waters: sources, mechanisms, impacts, and mitigation

Intensive technological developments, rapid population growth and urbanization, and excessive use of nitrogen fertilizers have caused water resources to be contaminated substantially by nitrates in Turkey. The accumulated information should be evaluated to draw a nationwide attention to the problem. The aim of this review article was to highlight the importance of nitrate (NO3) contamination and to discuss the measures to be taken to mitigate the contamination across the nation. Agriculture, especially chemical fertilizers used in irrigated agriculture, was the most important source of NO3 in groundwater. Also, the industrial and domestic discharges substantially contributed to NO3 in both groundwater and surface waters in many cases. The most severe and widespread groundwater (e.g., 344 mg NO3 L-1 in İzmir, 476 mg L-1 in Afyon, 477 mg L-1 in Antalya, and 948.0 mg L-1 in Konya) and surface water contaminations (e.g., 293.8 mg NO3 L-1 in İzmir, 63.3 mg L-1 in Eskişehir, 89.8 mg L-1 in Edirne, and 90.6 mg L-1 in Sakarya) occurred in the regions where intensive agriculture, industrial development, and rapid urbanization were clustered. Well-established irrigation and fertilizer management plans are critical for reducing fertilizer-related NO3 contaminations in the irrigated agriculture. Special attention should be given to the regions where industrially and domestically contaminated running water bodies are in contact with groundwater. Discharge of wastewaters to the streams, creeks, rivers, and lakes should be prevented. Well-designed studies are needed to evaluate potential health effects, including the risk of cancer, of NO3 in drinking water.

Application of machine learning in groundwater quality modeling - A comprehensive review

Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria, works that have used data-driven algorithms in predicting PTEs in groundwater are scarce. In addition, only a few works have simulated water quality indices using machine learning modelling methods in the region. Therefore, in this study, physicochemical analyses were carried out on groundwater samples in southeastern Nigeria. The laboratory results were used to compute two water quality indices: pollution index of groundwater (PIG) and the water pollution index (WPI), to ascertain groundwater quality. In addition, the physicochemical parameters served as input variables for multiple linear regression (MLR) and artificial neural network (ANN) modelling and prediction of Cr, Fe, Ni, NO3−, Pb, Zn, WPI, and PIG. The results of WPI and PIG computation showed that about 30–35% of the groundwater samples were unsuitable for human consumption, whereas 65–70% of the samples were deemed suitable. The insights from the PIG and WPI model also revealed that lead (Pb) was the most influential PTE that degraded the quality of groundwater resources in the research area. The findings of the MLR and ANN models indicated strong positive prediction accuracies (R 2 = 0.856–1.000) with low modeling errors. The predictive MLR and ANN models of the PIG and WPI generally outperformed those of the PTEs. The models produced in this study predicted the PTEs better compared to previous studies. Thus, this work provides insights into effective water sustainability, management, and protection.

Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater

Studies have shown that excessive intake of fluoride into human body from drinking water may cause fluorosis adversely affects teeth and bones. Fluoride in water is mostly of geological origin and the amounts depend highly on many factors such as availability and solubility of fluoride minerals as well as hydrogeological and geochemical conditions. Chemical methods usually accomplish fluoride analysis in drinking water. The chemical methods are expensive, labor-intensive and time-consuming in general although accurate and reliable results are obtained. An alternative cost-effective approach based on machine learning (ML) technique is investigated in this study. Furthermore, most effective input parameters are selected via proposed Simulated Annealing (SA) search scheme. Selected subset (SAR, K⁺, NO₃^-, NO₂^-, Mn, Ba and Fe) by SA algorithm exhibited high correlation coefficient values of 0.731 and strong t test scores of 5.248. On the other hand, most frequently selected individual features were identified as NO₃^-, NO₂^-, Fe and SAR by vote map. The results of experiments revealed that selected feature subset improves the prediction performance of the learning models while feature size is reduced substantially. Thus it eventually enabled determination of fluoride in a cheap, fast and feasible way.

Groundwater Quality: The Application of Artificial Intelligence

Humans and all other living things depend on having access to clean water, as it is an indispensable essential resource. Therefore, the development of a model that can predict water quality conditions in the future will have substantial societal and economic value. This can be accomplished by using a model that can predict future water quality circumstances. In this study, we employed a sophisticated artificial neural network (ANN) model. This study intends to develop a hybrid model of single exponential smoothing (SES) with bidirectional long short-term memory (BiLSTM) and an adaptive neurofuzzy inference system (ANFIS) to predict water quality (WQ) in different groundwater in the Al-Baha region of Saudi Arabia. Single exponential smoothing (SES) was employed as a preprocessing method to adjust the weight of the dataset, and the output from SES was processed using the BiLSTM and ANFIS models for predicting water quality. The data were randomly divided into two phases, training (70%) and testing (30%). Efficiency statistics were used to evaluate the SES-BiLSTM and SES-ANFIS models' prediction abilities. The results showed that while both the SES-BiLSTM and SES-ANFIS models performed well in predicting the water quality index (WQI), the SES-BiLSTM model performed best with accuracy (R = 99.95% and RMSE = 0.00910) at the testing phase, where the performance of the SES-ANFIS model was R = 99.95% and RMSE = 2.2941 × 100-07. The findings support the idea that the SES-BilSTM and SES-ANFIS models can be used to predict the WQI with high accuracy, which will help to enhance WQ. The results demonstrated that the SES-BiLSTM and SES-ANFIS models' forecasts are accurate and that both seasons' performances are consistent. Similar investigations of groundwater quality prediction for drinking purposes should benefit from the proposed SES-BiLSTM and SES-ANFIS models. Consequently, the results demonstrate that the proposed SES-BiLSTM and SES-ANFIS models are useful tools for predicting whether the groundwater in Al-Baha city is suitable for drinking and irrigation purposes.

Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling

Agricultural activities have become a major source of groundwater nitrate contamination. In this context, this study aims to analyse nitrate concentrations in a shallow aquifer of Mahdia-Kssour Essef in central-eastern Tunisia, identify the assignable sources, and predict the future levels using artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models. The results showed that nitrate concentrations measured in 21 pumping wells across the plain ranged from 17 to 521 mg L^-1. A total of 67% of the monitoring points greatly exceed the standard guideline value of 50 mg L^-1. The main relevant anthropogenic and natural factors, such as soil texture, land use, fertilizers application rates, livestock waste disposal, and groundwater table, are positively correlated with groundwater nitrate concentration. The ANN model showed good fitting between measured and simulated results with coefficient of determination (R²), root-mean-square error (RMSE), and mean absolute error (MAE) values of 0.88, 53.95, and 39.64, respectively. The ARIMA applied on annual average nitrate concentrations from 1998 to 2017 revealed that the best fitted model (p, d, q) is (1, 2, 1). The R² value is approximately 0.36, and the Theil inequality coefficient and bias proportion values are small and close to zero. These results proved the ARIMA model's adequacy in forecasting annual average nitrate concentrations of 116 mg L^-1 in 2025. These findings may be useful in making groundwater management decisions, particularly in rural and semi-arid areas, and the proposed ARIMA model could be used as a managed tool to monitor and reduce the nitrate intrusion into groundwater.

A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models

Various human activities have been the main causes of surface water pollution. The uneven distribution of industrial enterprises in the territories of the main river basins of Ukraine do not always allow the real state of the water quality to be assessed. This article has three purposes: (1) the modification of the Ukrainian method for assessing the WQI, taking into account the level of negative impact of the most dangerous chemical elements, (2) the modeling of WQI assessment using fuzzy logic and (3) the creation of an artificial neural network model for the prediction of the WQI. The fuzzy logic model used four input variables and calculated one output variable (WQI). In the final stage of the study, six ANN models were analyzed, which differed from each other in various loss function optimizers and activation functions. The optimal results were shown using an ANN with the softmax activation function and Adam’s loss function optimizer (MAPE = 9.6%; R2 = 0.964). A comparison of the MAPE and R2 indicators of the created ANN model with other models for assessing water quality showed that the level of agreement between the forecast and target data is satisfactory. The novelty of this study is in the proposal to modify the WQI assessment methodology which is used in Ukraine. At the same time, the phased and joint use of mathematical tools such as the fuzzy logic method and the ANN allow one to effectively evaluate and predict WQI values, respectively.

Modeling Groundwater Nitrate Contamination Using Artificial Neural Networks

The scope of the present study is the estimation of the concentration of nitrates (NO3−) in groundwater using artificial neural networks (ANNs) based on easily measurable in situ data. For the purpose of the current study, two feedforward neural networks were developed to determine whether including land use variables would improve the model results. In the first network, easily measurable field data were used, i.e., pH, electrical conductivity, water temperature, air temperature, and aquifer level. This model achieved a fairly good simulation based on the root mean squared error (RMSE in mg/L) and the Nash–Sutcliffe Model Efficiency (NSE) indicators (RMSE = 26.18, NSE = 0.54). In the second model, the percentages of different land uses in a radius of 1000 m from each well was included in an attempt to obtain a better description of nitrate transport in the aquifer system. When these variables were used, the performance of the model increased significantly (RMSE = 15.95, NSE = 0.70). For the development of the models, data from chemical and physical analyses of groundwater samples from wells located in the Kopaidian Plain and the wider area of the Asopos River Basin, both in Greece, were used. The simulation that the models achieved indicates that they are a potentially useful tools for the estimation of groundwater contamination by nitrates and may therefore constitute a basis for the development of groundwater management plans.

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks.

Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale gas extraction site. The water quality index was determined from the obtained parameters. A secondary objective of the study was to test the capacity of the artificial neural network (ANN) methods to model the water quality index in groundwater. The number of ANN input parameters was optimized and limited to seven, which was derived using a multiple regression model. Subsequently, using the stepwise regression method, models with ever fewer variables were tested. The best parameters were obtained for a network with five input neurons (electrical conductivity, pH as well as calcium, magnesium and sodium ions), in addition to five neurons in the hidden layer. The results showed that the use of the parameters is a convenient approach to modeling water quality index with satisfactory and appropriate accuracy. Artificial neural network methods exhibited the capacity to predict water quality index at the desirable level of accuracy (RMSE = 0.651258, R = 0.9992 and R2 = 0.9984). Neural network models can thus be used to directly predict the quality of groundwater, particularly in industrial areas. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. The novelty of these studies is the use of the ANN network to forecast WQI groundwater in an area in eastern Poland that was not previously studied—in Lublin.

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO₄) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

Modelling and Prediction of Water Quality by Using Artificial Intelligence

Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.

Water Quality Prediction Using Artificial Intelligence Algorithms

During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K-nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient (RNARNET = 96.17% and RLSTM = 94.21%). This kind of promising research can contribute significantly to water management.

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., Ca 2 + , Mg 2 + , Na + , K + , HCO 3 - , CO 3 2 - , SO 4 2 - , and Cl - concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na⁺ and Ca²⁺ showed the greatest positive and negative correlations with fluoride (r = 0.59 and -0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.

Modeling groundwater quality by using hybrid intelligent and geostatistical methods

Simulation of groundwater quality is important for managing water resources and mitigating water shortages, especially in arid and semiarid areas. Geostatistical models have been used for spatial prediction and interpolation of groundwater parameters. Recently, hybrid intelligent models have been employed for the simulation of dynamic systems. In this study, hybrid intelligent models, based on a neuro-fuzzy system integrated with fuzzy c-means data clustering (FCM) and grid partition (GP) models as well as artificial neural networks integrated with particle swarm optimization algorithm, were used to predict the spatial distribution of chlorine (Cl), electrical conductivity (EC), and sodium absorption ratio (SAR) parameters of groundwater. Results of the hybrid models were compared with geostatistical methods, including kriging, inverse distance weighting (IDW), and radial basis function (RBF). The latitude and longitude values of observation wells and qualitative parameters in three states of maximum, average, and minimum were introduced as input and output to the models, respectively. To evaluate the models, the root mean squared error (RMSE), the mean absolute error (MAE), and CC statistical criteria were used. Results showed that in the hybrid models, NF-GP with the lowest RMSE and MAE and highest CC was the most suitable model for the prediction of water quality parameters. The RMSE, MAE, and CC values were 107.175 (mg/L), 79.804 (mg/L), and 0.924 in the average state for Cl; were 518.544 (μmho/cm), 444.152 (μmho/cm), and 0.882 for electrical conductivity; and were 1.596, 1.350, and 0.582 for sodium absorption ratio, respectively. Among the geostatistical models, the kriging was found more accurate. Using the coordinates of wells will eventually allow the NF-GP to be used for more sampling and replace the visual techniques that require more time, cost, and facilities.

Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment

This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

Post-irrigation degradation of land and environmental resources in the Harran plain, southeastern Turkey

Irrigation is a key factor in plant production systems. However, excessive and inappropriate water and soil management systems can cause significant environmental problems. The GAP (the Southeastern Anatolia Project, SEAP) is a multisectoral integrated regional development project. It aims to improve the economical and social welfare of the region as best as possible. The two main objectives of the GAP project include irrigation and energy production. Irrigation was introduced to the Harran plain in 1995, and it led to significant changes in the land use patterns. The use of high-yielding crop varieties and chemical inputs (fertilizers and pesticide usage) resulted in important increases in plant production. Conversely, there was also an increase in land mismanagement. This included practices such as excessive irrigation, intensive soil tillage, insufficient carbon, and soil nutrient cycling. These mismanagement practices lead to soil degradation, which in turn causes increased salinity in soil and groundwater, sediment and nutrient transportation with runoffs, soil erosion, contamination of surfaces and subsurface water sources with nitrates and pesticides, and greenhouse gas emissions. In order to balance yield losses due to the decreasing soil quality, fertilizers and other chemicals were used extensively. This considerably contributed to the environmental problems. Additionally, increasing welfare and population propagated urbanization on arable lands, i.e., the construction of houses, factories, and other agricultural facilities. This further degraded the land and the environment. In conclusion, land irrigation led to production increases, but at the expense of degradation in the environment and soil quality. Moreover, land degradation occurred and further degraded the environment. It is extremely important to improve soil and water management in order to minimize these impacts. The forementioned problems could be solved by improving irrigation efficiencies, good soil and water management strategies, formation of modern well-managed irrigation districts, and educating farmers. Agricultural subsidy-based sanctions could enable these solutions. This study used archived data and evaluations of earlier studies to examine important agroenvironmental influences of introducing irrigation in the Harran plain.

A novel elemental sulfur-based mixotrophic denitrifying membrane bioreactor for simultaneous Cr(VI) and nitrate reduction

This study aims at investigating the simultaneous nitrate and chromate reduction by combining the advantages of sulfur-based autotrophic denitrification, heterotrophic denitrification and membrane bioreactor (MBR) technologies. A laboratory-scale MBR equipped with hydrophilic flat sheet polyethersulfone (PES) membranes (0.45μm) was used to evaluate the performance of mixotrophic denitrification at varying nitrate and Cr(VI) concentrations. Methanol was supplied at C/N (mg methanol/mg NO₃^--N) ratio of 1.33. In the absence of Cr(VI), almost complete denitrification of 50mg/L NO₃^--N was obtained and the methanol requirement (3.60±0.9mg COD/(mg NO₃^--N)) for heterotrophic denitrifiers, was quite close to the theoretical value (3.7mg COD/(mg NO₃^--N)). Around 54% of the influent nitrate was denitrified by heterotrophs and the rest (56%) was denitrified by autotrophic sulfur oxidizers. The effluent sulfate averaged around 200mg/L, which was below than the theoretical sulfate concentration if autotrophic denitrification process was used alone. Autotrophic denitrification activity completely ceased at 5mg/L Cr(VI), but heterotrophic denitrification did not show any inhibition. Almost complete chromate and nitrate reduction was observed at 1mg/L Cr(VI). MBR was operated for around 200days and a weekly physical membrane cleaning was enough at a flux of 15 LMH.

Heterotrophic-autotrophic sequential system for reductive nitrate and perchlorate removal

Nitrate and perchlorate were identified as significant water contaminants all over the world. This study aims at evaluating the performances of the heterotrophic-autotrophic sequential denitrification process for reductive nitrate and perchlorate removal from drinking water. The reduced nitrate concentration in the heterotrophic reactor increased with increasing methanol concentrations and the remaining nitrate/nitrite was further removed in the following autotrophic denitrifying process. The performances of the sequential process were studied under varying nitrate loads of [Formula: see text] at a fixed hydraulic retention time of 2 h. The C/N ratio in the heterotrophic reactor varied between 1.24 and 2.77 throughout the study. Nitrate and perchlorate reduced completely with maximum initial concentrations of [Formula: see text] and 1000 µg/L, respectively. The maximum denitrification rate for the heterotrophic reactor was [Formula: see text] when the bioreactor was fed with [Formula: see text] and 277 mg/L methanol. For the autotrophic reactor, the highest denitrification rate was [Formula: see text] in the first period when the heterotrophic reactor performance was low. Perchlorate reduction was initiated in the heterotrophic reactor, but completed in the following autotrophic process. Effluent sulphate concentration was below the drinking water standard level of 250 mg/L and pH was in the neutral level.

Modeling water quality in an urban river using hydrological factors--data driven approaches

Contrasting seasonal variations occur in river flow and water quality as a result of short duration, severe intensity storms and typhoons in Taiwan. Sudden changes in river flow caused by impending extreme events may impose serious degradation on river water quality and fateful impacts on ecosystems. Water quality is measured in a monthly/quarterly scale, and therefore an estimation of water quality in a daily scale would be of good help for timely river pollution management. This study proposes a systematic analysis scheme (SAS) to assess the spatio-temporal interrelation of water quality in an urban river and construct water quality estimation models using two static and one dynamic artificial neural networks (ANNs) coupled with the Gamma test (GT) based on water quality, hydrological and economic data. The Dahan River basin in Taiwan is the study area. Ammonia nitrogen (NH3-N) is considered as the representative parameter, a correlative indicator in judging the contamination level over the study. Key factors the most closely related to the representative parameter (NH3-N) are extracted by the Gamma test for modeling NH3-N concentration, and as a result, four hydrological factors (discharge, days w/o discharge, water temperature and rainfall) are identified as model inputs. The modeling results demonstrate that the nonlinear autoregressive with exogenous input (NARX) network furnished with recurrent connections can accurately estimate NH3-N concentration with a very high coefficient of efficiency value (0.926) and a low RMSE value (0.386 mg/l). Besides, the NARX network can suitably catch peak values that mainly occur in dry periods (September-April in the study area), which is particularly important to water pollution treatment. The proposed SAS suggests a promising approach to reliably modeling the spatio-temporal NH3-N concentration based solely on hydrological data, without using water quality sampling data. It is worth noticing that such estimation can be made in a much shorter time interval of interest (span from a monthly scale to a daily scale) because hydrological data are long-term collected in a daily scale. The proposed SAS favorably makes NH3-N concentration estimation much easier (with only hydrological field sampling) and more efficient (in shorter time intervals), which can substantially help river managers interpret and estimate water quality responses to natural and/or manmade pollution in a more effective and timely way for river pollution management.

Kinetics of autotrophic denitrification process and the impact of sulphur/limestone ratio on the process performance

Kinetics of sulphur-limestone autotrophic denitrification process in batch assays and the impact of sulphur/limestone ratio on the process performance in long-term operated packed-bed bioreactors were evaluated. The specific nitrate and nitrite reduction rates increased almost linearly with the increasing initial nitrate and nitrite concentrations, respectively. The process performance was evaluated in three parallel packed-bed bioreactors filled with different sulphur/limestone ratios (1:1, 2:1 and 3:1, v/v). Performances of the bioreactors were studied under varying nitrate loadings (0.05 - 0.80 gNO(-)(3) - NL⁻¹ d⁻¹) and hydraulic retention times (3-12 h). The maximum nitrate reduction rate of 0.66 g L⁻¹ d⁻¹ was observed at the loading rate of 0.80 g NO(-)(3) - N L⁻¹ d⁻¹ in the reactor with sulphur/limestone ratio of 3:1. Throughout the study, nitrite concentrations remained quite low (i.e. below 0.5 mg L⁻¹ NO(-)(2) -N. The reactor performance increased in the order of sulphur/limestone ratio of 3:1, 2:1 and 1:1. Denaturing gradient gel electrophoresis analysis of 16S rRNA genes showed quite stable communities in the reactors with the presence of Methylo virgulaligni, Sulfurimonas autotrophica, Sulfurovum lithotrophicum, Thiobacillus aquaesulis and Sulfurimonas autotrophica related species.

Heterotrophic and elemental-sulfur-based autotrophic denitrification processes for simultaneous nitrate and Cr(VI) reduction

Nitrate and chromate can be present together in water resources as nitrate is a common co-contaminant in surface and ground waters. This study aims at comparatively evaluating simultaneous chromate and nitrate reduction in heterotrophic and sulfur-based autotrophic denitrifying column bioreactors. In sulfur-based autotrophic denitrification process, elemental sulfur and nitrate act as an electron donor and an acceptor, respectively, without requirement of organic supplementation. Autotrophic denitrification was complete and not adversely affected by chromate up to 0.5 mg/L. Effluent chromate concentration was <50 μg/L provided that influent chromate concentration was ≤0.5 mg/L. Heterotrophic denitrification performance was not adversely affected even at 20 mg/L chromate and complete chromate reduction was attained up to 10 mg/L. Although autotrophic denitrification rate was much lower compared with heterotrophic one, it may be preferred in drinking water treatment due to the elimination of organic supplementation and the risk of treated effluent contamination.

Modeling of nitrate concentration in groundwater using artificial intelligence approach--a case study of Gaza coastal aquifer

Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making.

Nitrate and bromate removal by autotrophic and heterotrophic denitrification processes: batch experiments

The effects of various parameters on bromate reduction were tested using lab-scale batch reactors with sulfur based autotrophic and methanol based heterotrophic denitrification processes. The initial bromate (BrO3-) concentration of 100 and 500 μg/L was completely reduced and bromide (Br-) was produced stoichiometrically from bromate in all batch reactors. In all experiments, nitrate was completely reduced to below detection limit. Kinetic studies showed that the sulfur-based autotrophic nitrate reduction rate increased with increasing initial nitrate concentration. At stoichiometrically sufficient methanol concentration as an external carbon source, nitrate and bromate were reduced to below US EPA drinking water limits in heterotrophic denitrification conditions. The methanol was completely depleted at the end of the heterotrophic operation conditions.

Simultaneous bioreduction of nitrate and chromate using sulfur-based mixotrophic denitrification process

This study aims at evaluating simultaneous chromate and nitrate reduction using sulfur-based mixotrophic denitrification process in a column reactor packed with elemental sulfur and activated carbon. The reactor was supplemented with methanol at C/N ratio of 1.33 or 2. Almost complete denitrification was achieved at influent NO3(-)-N and Cr(VI) concentrations of 75 mg/L and 10mg/L, respectively, and 3.7h HRT. Maximum denitrification rate was 0.5 g NO3(-)-N/(L.d) when the bioreactor was fed with 75 mg/L NO3(-)-N, 150 mg/L methanol and 10mg/L Cr(VI). The share of autotrophic denitrification was between 12% and 50% depending on HRT, C/N ratio and Cr(VI) concentration. Effluent total chromium was below 50 μg/L provided that influent Cr(VI) concentration was equal or below 5mg/L. DGGE results showed stable microbial community throughout the operation and the presence of sulfur oxidizing denitrifying bacteria (Thiobacillus denitrificans) and Cr(VI) reducing bacteria (Exiguobacterium spp.) in the column bed.

Verifiable metamodels for nitrate losses to drains and groundwater in the Corn Belt, USA

Nitrate leaching in the unsaturated zone poses a risk to groundwater, whereas nitrate in tile drainage is conveyed directly to streams. We developed metamodels (MMs) consisting of artificial neural networks to simplify and upscale mechanistic fate and transport models for prediction of nitrate losses by drains and leaching in the Corn Belt, USA. The two final MMs predicted nitrate concentration and flux, respectively, in the shallow subsurface. Because each MM considered both tile drainage and leaching, they represent an integrated approach to vulnerability assessment. The MMs used readily available data comprising farm fertilizer nitrogen (N), weather data, and soil properties as inputs; therefore, they were well suited for regional extrapolation. The MMs effectively related the outputs of the underlying mechanistic model (Root Zone Water Quality Model) to the inputs (R(2) = 0.986 for the nitrate concentration MM). Predicted nitrate concentration was compared with measured nitrate in 38 samples of recently recharged groundwater, yielding a Pearson's r of 0.466 (p = 0.003). Predicted nitrate generally was higher than that measured in groundwater, possibly as a result of the time-lag for modern recharge to reach well screens, denitrification in groundwater, or interception of recharge by tile drains. In a qualitative comparison, predicted nitrate concentration also compared favorably with results from a previous regression model that predicted total N in streams.

Simultaneous heterotrophic and sulfur-oxidizing autotrophic denitrification process for drinking water treatment: control of sulfate production

A long-term performance of a packed-bed bioreactor containing sulfur and limestone was evaluated for the denitrification of drinking water. Autotrophic denitrification rate was limited by the slow dissolution rate of sulfur and limestone. Dissolution of limestone for alkalinity supplementation increased hardness due to release of Ca(2+). Sulfate production is the main disadvantage of the sulfur autotrophic denitrification process. The effluent sulfate concentration was reduced to values below drinking water guidelines by stimulating the simultaneous heterotrophic and autotrophic denitrification with methanol supplementation. Complete removal of 75 mg/L NO(3)-N with effluent sulfate concentration of around 225 mg/L was achieved when methanol was supplemented at methanol/NO(3)-N ratio of 1.67 (mg/mg), which was much lower than the theoretical value of 2.47 for heterotrophic denitrification. Batch studies showed that sulfur-based autotrophic NO(2)-N reduction rate was around three times lower than the reduction rate of NO(3)-N, which led to NO(2)-N accumulation at high loadings.