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

Machine Learning Modeling Based on Microbial Community for Prediction of Natural Attenuation in Groundwater

Natural attenuation is widely adopted as a remediation strategy, and the attenuation potential is crucial to evaluate whether remediation goals can be achieved within the specified time. In this work, long-term monitoring of indigenous microbial communities as well as benzene, toluene, ethylbenzene, and xylene (BTEX) and chlorinated aliphatic hydrocarbons (CAHs) in groundwater was conducted at a historic pesticide manufacturing site. A machine learning approach for natural attenuation prediction was developed with random forest classification (RFC) followed by either random forest regression (RFR) or artificial neural networks (ANNs), utilizing microbiological information and contaminant attenuation rates for model training and cross-validation. Results showed that the RFC could accurately predict the feasibility of natural attenuation for both BTEX and CAHs, and it could successfully identify the key genera. The RFR model was sufficient for the BTEX natural attenuation rate prediction but unreliable for CAHs. The ANN model showed better performance in the prediction of the attenuation rates for both BTEX and CAHs. Based on the assessments, a composite modeling method of RFC and ANN was proposed, which could reduce the mean absolute percentage errors. This study reveals that the combined machine learning approach under the synergistic use of field microbial data has promising potential for predicting natural attenuation.

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.

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.

A Review of the Artificial Neural Network Models for Water Quality Prediction

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm

Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO₃^-, F^-, Cl^-, SO₄^2-, Na⁺, K⁺, Mg²⁺, and Ca²⁺ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.

Statistical Learning Methods for Classification and Prediction of Groundwater Quality Using a Small Data Record

The objective of this study was to consider the efficiency of support vector machine (SVM) and artificial neural network (ANN) for the classification and prediction of groundwater quality using a small data record in Malayer, Iran. For this purpose, 14 groundwater quality variables that had been collected from 27 groundwater sampling wells were used. Cluster analysis discriminated the total sampling stations into two groups. The classification was implemented by SVM using polynomial and RBF kernel methods. The respective sensitivity and specificity of this model were 0.89 and 0.80 while that of positive predictive value and negative predictive value were 0.89 and 0.86, respectively. The prediction of water quality index (WQI) was implemented using ANN. Despite the high correlation coefficient between the predicted and observed values of WQI(r = 0.90), the generalization ability of this model was low(r = 0.60) indicating the over-fitting of the model to the training data set.

Evaluating impacts of recharging partially treated wastewater on groundwater aquifer in semi-arid region by integration of monitoring program and GIS technique

The current study investigates the impact of recharging of partially treated wastewater through an infiltration basin on the groundwater aquifer quality parameters. A monitoring program supported by a geographic information analysis (GIS) tool was used to conduct this study. Groundwater samples from the entire surrounding boreholes located downstream the infiltration basin, in addition to samples from the recharged wastewater coming from the Beit Lahia wastewater treatment (BLWWTP), were monitored and analysed between 2011 and 2014. The analysis was then compared with the available historical data since 2008. Results revealed a groundwater replenishment with the groundwater level increased by 1.0-2.0 m during the study period. It also showed a slight improvement in the groundwater quality parameters, mainly a decrease in TDS, Cl^- and NO₃^- levels by 5.5, 17.1 and 20%, respectively, resulting from the relatively better quality of the recharged wastewater. Nevertheless, the level of boron and ammonium in the groundwater wells showed a significant increase over time by 96 and 100%, respectively. Moreover, the infiltration rate was slowed down in time due to the relatively high level of total suspended solid (TSS) in the infiltrated wastewater.