Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models

Rizeei, Hossein Mojaddadi; Azeez, Omer Saud; Pradhan, Biswajeet; Khamees, Hayder Hassan

doi:10.1007/s10661-018-7013-8

Cited by 45 publications

(17 citation statements)

References 55 publications

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“…The highest area is the limited probability of nitrate concentration ( Figure 8 ), despite the moderate to high susceptibility in the Marvdasht watershed. When we talk about the groundwater susceptibility model, different statistical and empirical models for predicting groundwater mineral concentrations have been reviewed over the last decades [ 75 , 76 , 77 ]. However, these susceptibility models have some limitations and assumptions, and recently data mining with machine learning approaches has been effectively popularized due to their ability to analyze the multifarious relationship between predictors and response [ 34 , 78 ].…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

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Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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“…Intrinsic vulnerability methods do not account for chemical specific characteristics; therefore, approaches that account for existing pollution have also been proposed in the literature. In particular, logistic regression models have been extensively used, albeit with a mixed degree of success, to calculate the probability of the exceedance of a pre-defined nitrogen (or some other contaminant) threshold [12,[16][17][18][19][20][21][22][23][24][25][26][27].The main reasons for the popularity of logistic regression techniques for aquifer vulnerability assessment include its ability to deal with censored data and the availability of computer programs to perform the necessary calculations. Furthermore, the resulting equation can be easily embedded into a geographic information system (GIS) to develop aquifer vulnerability maps.…”

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confidence: 99%

Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas

Uddameri

Silva

Singaraju

et al. 2020

Water

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The performance of four tree-based classification techniques-classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths-an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.Water 2020, 12, 1023 2 of 27 worldwide [5,6]. Intensification of agricultural activities for both food and energy will further increase the risks of nitrate contamination in aquifers across the world [7][8][9].Nitrate is mobile and fairly recalcitrant, especially in shallow groundwater systems that typically tend to be under oxidizing conditions. Nitrate exhibits the ability to spread over large areas and cannot be treated in-situ using conventional plume scale treatment technologies [10]. Therefore, individual homeowners are often required to install costly point-of-use treatment systems to mitigate nitrate risks arising from the ingestion of contaminated groundwater [11,12]. However, as nitrate is colorless and odorless, many people do not realize the risk of nitrate contamination and are unwittingly exposed to elevated levels of nitrate over long periods of time [13]. Therefore, nitrate contamination must be prevented through proper land management practices. Additionally, areas with a high susceptibility to nitrate pollution must be carefully delineated, with the goal of increasing public awareness regarding elevated health risks arising from nitrate exposures. Such an effort is also useful to prioritize monitoring activities and ensure that the limited fiscal and logistic resources are being used in a prudent manner.Mapping the susceptibility of aquifers to nitrate contamination is an essential step in mitigating and managing nitrate contamination. Multi-criteria decision making (MCDM) methods, such as DRASTIC [14], have been widely used to map aquifer vulnerabili...

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“…Thus, ensuring the groundwater safety through the advancement of systematic quality control is considered crucial to the health of several ecosystems as well as human development [5]. Unlike surface water resources, groundwater is more vulnerable to disturbances and contaminations, as it takes a very long time and great cost to recover [6][7][8]. Therefore, predictive modeling and prevention strategies have gained popularity to empower policymakers for efficient groundwater governance through informed decisions and recommendations [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

Mosavi

Hosseini

Choubin

et al. 2020

Water

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Groundwater resources, unlike surface water, are more vulnerable to disturbances and contaminations, as they take a very long time and significant cost to recover. So, predictive modeling and prevention strategies can empower policymakers for efficient groundwater governance through informed decisions and recommendations. Due to the importance of groundwater quality modeling, the hardness susceptibility mapping using machine learning (ML) models has not been explored. For the first time, the current research aimed to predict groundwater hardness susceptibility using the ML models. The performance of two ensemble models of boosted regression trees (BRT) and random forest (RF) is investigated through the arrangement of a comparative study with multivariate discriminant analysis (MDA). According to the hardness values in 135 groundwater quality monitoring wells, the hard and soft water are determined; then, 11 predictor variables including distance from the sea (DFS), land use, elevation, distance from the river (DFR), depth to groundwater (DTGW), pH, precipitation (PCP), evaporation (E), groundwater level (GWL), curvature, and lithology are used for predicting the groundwater hardness susceptibility map. Results indicated that the variables of DFR, DTGW, elevation, and DFS had a higher contribution to the modeling process. So, the high harness areas are mostly related to low elevations, low DTGW, and proximity to river and sea, which facilitate the percolation conditions for minerals containing calcium or magnesium into groundwater.

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Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models

Cited by 45 publications

References 55 publications

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

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