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

Abstract

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.

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

“…However, numerical models require large amounts of specific data, have a complex structure, and are time‐consuming to calibrate (Barzegar et al , ). These limitations restrict their application, especially in the context of developing countries (Coppola et al ; Alagha et al ). In recent decades, machine learning algorithms have attracted attention due to their ability to support predictions in different fields of hydrology and earth system sciences.…”

“…In predicting groundwater contamination, the capacity of machine learning algorithms to predict contaminants by drawing upon large volumes of nonlinear and complex data, from various sources, at different scales, is a significant asset (Chau ; Mirabbasi ). Recently, some popular machine learning algorithms including artificial neural networks (ANN) (Sahoo et al ; Yesilnacar et al ; Chowdhury et al ; Al‐Mahallawi et al ; Dar et al ; Sirat ; Alagha et al ; Barzegar and Asghari Moghaddam ), support vector machine (SVM) (Alagha et al ; Arabgol et al ; Barzegar and Asghari Moghaddam ), weighted projection regression (WPR) (Khalil et al ), and extreme learning machine (ELM) (Zhang et al ) have been used to predict groundwater contamination by nitrate, fluoride, and arsenic.…”

Piper-PCA-Fisher Recognition Model of Water Inrush Source: A Case Study of the Jiaozuo Mining Area

“…Therefore, 28 typical water samples (1,3,4,5,6,8,11,12,13,15,16,17,18,20,21,23,24,25,26,27,30,31,32,33,34,35,36, and 37) in Table 1 were finally confirmed as water samples for training the water inrush source recognition model in the next step.…”

“…Methods of water inrush source recognition used in mines in recent years include hydrochemical characteristic component analysis, isotype analysis, artificial tracing, multivariate statistical analysis, multiclass clustering functions, environmental isotope [1][2][3][4][5][6][7][8][9][10][11][12], and other relatively typical analysis methods. The following methods are applied mostly in multivariate statistical analysis: hierarchical clustering linear discriminant method based on Fisher theory [13][14][15] and hydrochemical concentration forecast method based on artificial neural networks [16][17][18]. The distance discriminant analysis model was established to effectively predict the sources of mine water inrush at the base of the Jiaozuo mining area through measured data by selecting six discrimination factors [19].…”

The application of Dempster–Shafer theory of evidence for assessing groundwater vulnerability at Galal Badra basin, Wasit governorate, east of Iraq

“…Due to the high mobility and solubility, nitrate NO 3 always exists in groundwater under oxidizing conditions (Almasri and Ghabayen 2008). In general, source of nitrate in groundwater can be classified into point and non-point sources (Alagha et al 2013). The non-point source of nitrate includes fertilizer, manures, and return flows from irrigation, while the point sources include septic system and cesspits.…”