Modeling groundwater quality by using hybrid intelligent and geostatistical methods

Advancing groundwater quality predictions: Machine learning challenges and solutions

Machine learning (ML) is revolutionizing groundwater quality research by enhancing predictive accuracy and management strategies for contamination. This comprehensive review explores the evolution of ML technologies and their integration into environmental science, assessing 230 papers to understand the advancements and challenges in groundwater quality research. It reveals that a substantial portion of the research neglects critical preprocessing steps, crucial for model accuracy, with 83 % of the studies overlooking this phase. Furthermore, while model optimization is more commonly addressed, being implemented in 65 % of the papers, there is a noticeable gap in model interpretability, with only 15 % of the research providing explanations for model outcomes. Comparative evaluation of ML algorithms and careful selection of evaluation metrics are deemed essential for determining model fitness and reliability. The review underscores the need for interdisciplinary collaboration, methodological rigor, and continuous innovation to advance ML in groundwater management. By addressing these challenges and implementing solutions, the full potential of ML can be harnessed to tackle complex environmental issues and ensure sustainable groundwater management. This comprehensive and critical review paper can serve as a guiding framework to establish minimum standards for developing ML in groundwater quality studies.

Providing predictive models for quality parameters of groundwater resources in arid areas of central Iran: A case study of kashan plain

Groundwater pollution can occur due to both anthropogenic and natural causes, leading to a decline in water quality and posing a threat to human health and the environment. The pollution of ground water resources with chemical pollutants is often considered. To manage water resources sustainably, ensuring their quality and quantity is crucial. Yet, testing groundwater can be expensive and time-consuming. So, using modeling to predict the chemical parameters of groundwater resources is considered to be an efficient and economical method. In this study, we examined three models to predict groundwater quality in dry regions by using R programming language. The random forest (RF) outperformed the other models in developing predictive models for water quality. Also, the multiple linear regression (MLR) model demonstrated strong performance, particularly in predicting total hardness (TH) in Aran Va Bidgol groundwater resources. The decision tree (DT) model did well but had lower performance than the RF model in predicting quality parameters. This approach can be efficacious in the field of effective management and protection of groundwater resources and enables the assessment of risks related to water resources.

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.

Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms

In recent decades, the simulation and modeling of water quality parameters have been useful for monitoring and assessment of the quality of water resources. Moreover, the use of multiple modeling techniques, rather than a standalone model, tends to provide more robust and reliable insights. In this present paper, several soft computing techniques were integrated and compared for the modeling of groundwater quality parameters (pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), modified heavy metal index (MHMI), pollution load index (PLI), and synthetic pollution index (SPI)) in Ojoto area, SE Nigeria. Standard methods were employed in the physicochemical analysis of the groundwater resources. It was found that anthropogenic and non-anthropogenic activities influenced the concentrations of the water quality parameters. The PLI, MHMI, and SPI revealed that about 20-25% of the groundwater samples are unsuitable for drinking. Simple linear regression indicated that strong agreements exist between the results of the water quality indices. Principal component and Varimax-rotated factor analyses showed that Pb, Ni, and Zn influenced the judgment of the water quality indices most. Q-mode hierarchical and K-means clustering  algorithms grouped the water samples based on their pH, EC, TDS, TH, MHMI, PLI, and SPI values. Multiple linear regression (MLR) and artificial neural network (ANN) algorithms were used for the simulation and prediction of  the pH, EC, TDS, TH, PLI, MHMI, and SPI. The MLR performed better than the ANN model in predicting EC, TH, and TDS. Nevertheless, the ANN model predicted the pH better than the MLR model. Meanwhile, both MLR and ANN performed equally in the prediction of PLI, MHMI, and SPI.

Groundwater quality modeling using a novel hybrid data-intelligence model based on gray wolf optimization algorithm and multi-layer perceptron artificial neural network: a case study in Asadabad Plain, Hamedan, Iran

Monitoring and assessment of groundwater quality (GWQ) as an important freshwater source for drinking purposes in urban and rural regions of developing countries due to rapidly increasing contamination is one of the concerns of water managers. Therefore, developing an efficient intelligent model for analyzing GWQ could help hydro-environmental engineers for sustainable water supply. The current research investigated the applicability of a novel nature-inspired optimization algorithm hybridized with multi-layer perceptron artificial neural network based on gray wolf optimization (GWO) for estimating dissolved oxygen (DO) total dissolved solid (TDS) and turbidity parameters at Asadabad Plain, Iran, and results are compared with the stand-alone multi-layer perceptron artificial neural network (MLPANN), generalized regression neural network (GRNN), and multiple linear regression (MLR) approaches. Evaluation of performance of models is carried out using various statistical indices like relative root mean square error, Nash-Sutcliffe efficiency, and correlation coefficient. Based on the results obtained, it is found that the hybrid GWO-MLPANN is a beneficial GWQ forecasting tool in accordance to high performance accuracy. Also, the study found that the superiority of the applied meta-heuristic algorithm (GWO) in improving the performance accuracy of the stand-alone artificial intelligence techniques in modeling the GWQ parameters.

Simulating contaminant transport in unsaturated and saturated groundwater zones

The demand for clean and adequate water is rising rapidly with increasing population. This growing demand for water necessitates the measurement of the quantity and quality of water. Simulation modeling has become increasingly popular in the last two decades largely because of their predictive ability. This paper reviews the approaches for simulation modeling in groundwater resources management, focusing on models that have been used to simulate contaminant transport through the aquifer system. Recent research papers that have integrated the models for unsaturated and saturated zones have also been studied and described. Integrated models require assessment of the complex interactions between the groundwater zones and the movement of water and solute through them. Due to this, integrated models provide a more accurate modeling approach than models that have been independently developed for saturated and unsaturated zones. Application of such models is encouraged at the regional level to arrive at the best groundwater management decisions. PRACTITIONER POINTS: In the past few decades, modeling of contaminant transport in groundwater systems has seen tremendous applications. A number of models exist that independently simulate flow and solute transport in unsaturated and saturated zones. Recently, focus has been given on developing advanced coupled modeling approaches that require less inputs and run times.

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.