Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques

Real-time prediction of the chemical oxygen demand component parameters in activated sludge model using backpropagation neural network

Activated sludge models are increasingly being adopted to guide the operation of wastewater treatment plants. Chemical oxygen demand (COD) is an indispensable input for such models. To ensure that the activated sludge mathematical model can adapt to various water quality conditions and minimize prediction errors, it is essential to predict the parameters of the COD components in real-time based on the actual influent COD concentrations. However, conventional methods of determining the components' contributions are too intricate and time-consuming to be really useful. In this study, the chemical oxygen demand in the actual waste water treatment plant was disassembled and analyzed. The research involved determining the proportions of each COD component, assessing the reliability of the measurement parameters, and examining potential factors affecting measurement accuracy, including weather conditions, pipeline conditions, and residents' habits. Then, a backpropagation neural network was developed which can deliver real-time predictions for five important contributors to COD in real time. In addition, using the receiver operating characteristics curve and prediction accuracy to evaluate the performance of the prediction model. For all five components, which SS, XS, SI, XA, and XH, the prediction accuracy of model was more than 80 %. The maximum deviation values of these parameters fall within the range of the actual detected values, suggesting that the model's predictions align well with real-world observations, and demonstrated prediction performance adequate for practical application in wastewater treatment. This article can provide research basis for the engineering application of activated sludge model and help for the intelligent upgrading of waste water treatment plants.

Research on SVR Water Quality Prediction Model Based on Improved Sparrow Search Algorithm

Multiparameter water quality trend prediction technique is one of the important tools for water environment management and regulation. This study proposes a new water quality prediction model with better prediction performance, which is combined with improved sparrow search algorithm (ISSA) and support vector regression (SVR) machine. For the problems of low population diversity and easily falling into local optimum of sparrow search algorithm (SSA), ISSA is proposed to increase the initial population diversity by introducing Skew-Tent mapping and to help the algorithm jump out of local optimum by using the adaptive elimination mechanism. The optimal values of the penalty factor C and kernel function parameter g of the SVR model are selected using ISSA to make the model have better prediction accuracy and generalization performance. The performance of the ISSA-SVR water quality prediction model is compared with BP neural network, SVR model, and other hybrid models by conducting water quality prediction experiments with actual breeding-water quality data. The experimental results showed that the prediction accuracy of the ISSA-SVR model was significantly higher than that of other models, reaching 99.2%; the mean square deviation (MSE) was 0.013, which was 79.37% lower than that of the SVR model and 75% lower than that of SSA-SVR model, and the coefficient of determination (R²) was 0.98, which was 5.38% higher than that of the SVR model and 7.57% higher than that of the SSA-SVR model, indicating that the ISSA-SVR water quality prediction model has some engineering application value in the field of water body management.

Assessment of the total organic carbon employing the different nature-inspired approaches in the Nakdong River, South Korea

Total organic carbon (TOC) has vital significance for measuring water quality in river streamflow. The detection of TOC can be considered as an important evaluation because of issues on human health and environmental indicators. This research utilized the novel hybrid models to improve the predictive accuracy of TOC at Andong and Changnyeong stations in the Nakdong River, South Korea. A data pre-processing approach (i.e., complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)) and evolutionary optimization algorithm (i.e., crow search algorithm (CSA)) were implemented for enhancing the accuracy and robustness of standalone models (i.e., multivariate adaptive regression spline (MARS) and M5Tree). Various water quality indicators (i.e., TOC, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), and suspended solids (SS)) were utilized for developing the standalone and hybrid models based on three input combinations (i.e., categories 1~3). The developed models were evaluated utilizing the correlation coefficient (CC), root-mean-square error (RMSE), and Nash-Sutcliffe efficiency (NSE). The CEEMDAN-MARS-CSA based on category 2 (C-M-CSA2) model (CC = 0.762, RMSE = 0.570 mg/L, and NSE = 0.520) was the most accurate for predicting TOC at Andong station, whereas the CEEMDAN-MARS-CSA based on category 3 (C-M-CSA3) model (CC = 0.900, RMSE = 0.675 mg/L, and NSE = 0.680) was the best at Changnyeong station.

The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa

Reliable prediction of water quality changes is a prerequisite for early water pollution control and is vital in environmental monitoring, ecosystem sustainability, and human health. This study uses Artificial Neural Network (ANN) technique to develop the best model fits to predict water quality parameters by employing multilayer perceptron (MLP) neural network and the radial basis function (RBF) neural network, using data collected from three district municipalities. Two input combination models, MLP-4-5-4 and MLP-4-9-4, were trained, verified, and tested for their predictive performance ability, and their physicochemical prediction accuracy was compared by using each model's observed data with the predicted data. The MLP-4-5-4 model showed a better understanding of the data sets and water quality predictive ability giving an MSE of 39.06589 and a correlation coefficient (R²) of the observed and the predicted water quality of 0.989383 compared to the MLP-4-9-4 model (R² = 0.993532, MSE = 39.03087). These results apply to natural water resources management in South Africa and similar catchment systems. The MLP-4-5-4 system can be scaled up for future water quality prediction of the Waste Water Treatment Plants (WWTPs), groundwater, and surface water while raising awareness among the public and industry on future water quality.

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.

Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things

Natural disasters have tended to increase and become more severe over the last decades. A preparation measure to cope with future floods is flood forecasting in each particular area for warning involved persons and resulting in the reduction of damage. Machine learning (ML) techniques have a great capability to model the nonlinear dynamic feature in hydrological processes, such as flood forecasts. Internet of Things (IoT) sensors are useful for carrying out the monitoring of natural environments. This study proposes a machine learning-based flood forecast model to predict average regional flood inundation depth in the Erren River basin in south Taiwan and to input the IoT sensor data into the ML model as input factors so that the model can be continuously revised and the forecasts can be closer to the current situation. The results show that adding IoT sensor data as input factors can reduce the model error, especially for those of high-flood-depth conditions, where their underestimations are significantly mitigated. Thus, the ML model can be on-line adjusted, and its forecasts can be visually assessed by using the IoT sensors’ inundation levels, so that the model’s accuracy and applicability in multi-step-ahead flood inundation forecasts are promoted.

Explore the relationship between fish community and environmental factors by machine learning techniques

In the face of multiple habitat alterations originating from both natural and anthropogenic factors, the fast-changing environments pose significant challenges for maintaining ecosystem integrity. Machine learning is a powerful tool for modeling complex non-linear systems through exploratory data analysis. This study aims at exploring a machine learning-based approach to relate environmental factors with fish community for achieving sustainable riverine ecosystem management. A large number of datasets upon a wide variety of eco-environmental variables including river flow, water quality, and species composition were collected at various monitoring stations along the Xindian River of Taiwan during 2005 and 2012. Then the complicated relationship and scientific essences of these heterogonous datasets are extracted using machine learning techniques to have a more holistic consideration in searching a guiding reference useful for maintaining river-ecosystem integrity. We evaluate and select critical environmental variables by the analysis of variance (ANOVA) and the Gamma test (GT), and then we apply the adaptive network-based fuzzy inference system (ANFIS) for an estimation of fish bio-diversity using the Shannon Index (SI). The results show that the correlation between model estimation and the biodiversity index is higher than 0.75. The GT results demonstrate that biochemical oxygen demand (BOD), water temperature, total phosphorus (TP), and nitrate-nitrogen (NO₃-N) are important variables for biodiversity modeling. The ANFIS results further indicate lower BOD, higher TP, and larger habitat (flow regimes) would generally provide a more suitable environment for the survival of fish species. The proposed methodology not only possesses a robust estimation capacity but also can explore the impacts of environmental variables on fish biodiversity. This study also demonstrates that machine learning is a promising avenue toward sustainable environmental management in river-ecosystem integrity.

Hydrodynamic and water quality modeling of a large floodplain lake (Poyang Lake) in China

Floodplain lakes are valuable to humans because of their various functions and are characterized by dramatic hydrological condition variations. In this study, a two-dimensional coupled hydrodynamic and water quality model was applied in a large floodplain lake (i.e., Poyang Lake), to investigate spatial and temporal water quality variations. The model was established based on detailed data such as lake terrain, hydrological, and water quality. Observed lake water level and discharge and water quality parameters (TN, TP, COD_Mn, and NH₄-N) were used to assess model performance. The hydrodynamic model results showed satisfactory results with R² and MRE values ranging between 0.96 and 0.99 and between 2.45 and 6.14%, respectively, for lake water level simulations. The water quality model basically captured the temporal variations in water quality parameters with R² of TN, TP, COD_Mn, and NH₄-N simulation ranges of 0.56-0.91, 0.44-0.66, 0.64-0.67, and 0.44-0.57, respectively, with TP of Xingzi Station and COD_Mn of Duchang Station excluded, which may be further optimized with supplementation of sewage and industrial discharge data. The modeled average TN, TP, COD_Mn, and NH₄-N concentrations across the lake were 1.36, 0.05, 1.99, and 0.48 mg/L, respectively. The modeled spatial variations of the lake showed that the main channel of the lake acted as a main pollutant passageway, and the east part of the lake suffered high level of pollution. In addition, consistent with previous water quality evaluations based on field investigations, water quality was the highest (average TN = 1.35 mg/L) during high water level periods and the poorest (average TN = 1.96 mg/L) during low water level periods. Scenario analysis showed that by decreasing discharge of upstream flow by 20% could result in the increase of TN and TP concentrations by 25.6% and 23.2% respectively. In summary, the model successfully reproduced the complex water and pollutant exchange processes in the systems involving upstream rivers, the Poyang Lake, and the Yangtze River. The model is beneficial for future modeling of the impact of different load reduction and other hydrological regime changes on water quality variation and provides a relevant example for floodplain lake management.

Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models

A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km2 located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding.

Adsorptive removal of arsenic by novel iron/olivine composite: Insights into preparation and adsorption process by response surface methodology and artificial neural network

Olivine, a low-cost natural material, impregnated with iron is introduced in the adsorptive removal of arsenic. A wet impregnation method and subsequent calcination were employed for the preparation of iron/olivine composite. The major preparation process parameter, viz., iron loading and calcination temperature were optimized through the response surface methodology coupled with a factorial design. A significant variation of adsorption capacity of arsenic (measured as total arsenic), i.e., 63.15 to 310.85 mg/kg for arsenite [As(III)^T] and 76.46 to 329.72 mg/kg for arsenate [As(V)^T] was observed, which exhibited the significant effect of the preparation process parameters on the adsorption potential. The iron loading delineated the optima at central points, whereas a monotonous decreasing trend of adsorption capacity for both the As(III)^T and As(V)^T was observed with the increasing calcination temperature. The variation of adsorption capacity with the increased iron loading is more at lower calcination temperature showing the interactive effect between the factors. The adsorbent prepared at the optimized condition of iron loading and calcination temperature, i.e., 10% and 200 °C, effectively removed the As(III)^T and As(V)^T by more than 96 and 99%, respectively. The material characterization of the adsorbent showed the formation of the iron compound in the olivine and increase in specific surface area to the tune of 10 multifold compared to the base material, which is conducive to the enhancement of the adsorption capacity. An artificial neural network was applied for the multivariate optimization of the adsorption process from the experimental data of the univariate optimization study and the optimized model showed low values of error functions and high R² values of more than 0.99 for As(III)^T and As(V)^T. The adsorption isotherm and kinetics followed Langmuir model and pseudo second order model, respectively demonstrating the chemisorption in this study.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R²=0.82), but it was not robust in extrapolation (R²=0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD.

Conservation of groundwater from over-exploitation-Scientific analyses for groundwater resources management

Groundwater over-exploitation has produced many critical problems in the southern Taiwan. The accumulated stresses and demands make groundwater management a complex issue that needs innovative scientific analyses for deriving better water management strategies. In this study, we aimed to provide scientific analyses of the groundwater systems in the Pingtung Plain through soft-computing techniques to explore its spatial-temporal and hydro-geological characteristics for the elaboration of future groundwater management plans and in decision-making process. We conducted a study to assess the essential features of the groundwater systems based on the long-term large datasets of regional groundwater levels by using the principal component analysis (PCA), and the self-organizing map (SOM) with regression analysis. The PCA results demonstrated that two leading components could well present the spatial characteristics of the groundwater systems and classify the region into eastern, western and transition zones. The SOM results could visibly explore the behavior of regional groundwater variations in various aquifers and the multi-relations among climate and hydrogeological variables. Results revealed that the potential of groundwater recharge made by precipitation or river flow was higher in the eastern zone than in the western zone. Analysis results further showed an increase of the groundwater levels in the western zone after year 2006, while there were no obvious increases of the groundwater levels in the eastern or transition zones. Based on the investigated characteristics, we suggest that a sound groundwater management plan should consider zonal difference of the groundwater systems to achieve groundwater conservation.