Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

Abstract

Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks.

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

“…Jiroft plain resembling, qualitatively, the distributions obtained with our model (Sajedi-Hosseini et al 2018;Matzeu et al 2017 andAwais et al 2021). Altogether, the results of our investigation and the observations made in previous work show that the high and very high risk of groundwater contamination in the central and south parts of Jiroft plain can be attributed to the overutilization of fertilizers in agricultural lands, and to waste production in the dense urban lands in the study area.However, one major difference between the approaches adopted in our study and in previous work(Sajedi-Hosseini et al 2018; Matzeu et al 2017 and Awais et al 2021) should be emphasized in this context.…”

“…Vulnerability map of groundwater contamination Figure 5 displays the groundwater vulnerability map obtained through the application of the modi ed-DRASTIC method. Hereby, vulnerability was categorized into ve classes: very low, low, moderate, high, and very high, following previous work (Awais et al 2021). As can be seen from Fig.…”

“…Furthermore, soil type and lithology exert important in uences on groundwater recharge and in ltration (Awais et al 2021). In the present study, the lithological map of the study region at 1:50000 scale was provided from the Geological Survey of Iran (Fig.…”

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

“…Remote Sens. 2022, 14, x FOR PEER REVIEW 3 of 20 machine learning classification algorithms have recently been established as state-of-theart methods for suitability prediction in various disciplines, such as agriculture [9], forestry [23], nature and environment conservation [24], including land and marine contamination studies [25,26]. While these methods have been successfully utilized in previous studies [27,28], habitat suitability prediction methods according to environmental criteria have been relatively unexplored, especially for the purpose of extending the habitat of endangered flora species [29].…”

“…To provide an efficient multispectral imaging solution with high spatial resolution in restricted locations, unmanned aerial systems (UASs) have been successfully implemented in various nature conservation studies, specifically ensuring non-invasive data collection in sensitive study areas [22]. Various machine learning classification algorithms have recently been established as state-of-the-art methods for suitability prediction in various disciplines, such as agriculture [9], forestry [23], nature and environment conservation [24], including land and marine contamination studies [25,26]. While these methods have been success-fully utilized in previous studies [27,28], habitat suitability prediction methods according to environmental criteria have been relatively unexplored, especially for the purpose of extending the habitat of endangered flora species [29].…”