Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan

Umair Rasool; Xinan Yin; Zongxue Xu; Muhammad Awais Rasool; Venkatramanan Senapathi; Mureed Hussain; Jamil Siddique; Juan Carlos Trabucco

doi:10.1016/j.chemosphere.2022.135265

Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan

Chemosphere. 2022 Sep;303(Pt 3):135265. doi: 10.1016/j.chemosphere.2022.135265. Epub 2022 Jun 9.

Authors

Umair Rasool¹, Xinan Yin², Zongxue Xu³, Muhammad Awais Rasool⁴, Venkatramanan Senapathi⁵, Mureed Hussain⁶, Jamil Siddique⁷, Juan Carlos Trabucco⁸

Affiliations

¹ State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing, 100875, China; College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing, 100875, China.
² State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing, 100875, China.
³ College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing, 100875, China. Electronic address: zxxu@bnu.edu.cn.
⁴ University of Agriculture, Faisalabad, Burewala Sub-campus, Punjab, Pakistan.
⁵ Department of Disaster Management, Alagappa University, Kariakudi, 630003, Tamil Nadu, India.
⁶ Lasbela University of Agriculture, Water and Marine Sciences, Uthal, Lasbela, Pakistan.
⁷ Earth Science Department, Quaid-I-Azam University, Islamabad, Pakistan.
⁸ Universidad Metropolitana - Department of Mathematics, Caracas, Venezuela.

PMID: 35691394
DOI: 10.1016/j.chemosphere.2022.135265

Abstract

Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain.

Keywords: GW factors; Groundwater productivity potential; Machine learning; Quetta valley; ROC/AUC; Standard error.

MeSH terms

Algorithms
Bayes Theorem
Groundwater*
Machine Learning*
Pakistan