TY - JOUR
T1 - AI-Powered Water Quality Index Prediction
T2 - Unveiling Machine Learning Precision in Hyper-Arid Regions
AU - Ahmad, Tofeeq
AU - Ali, Luqman
AU - Alshamsi, Dalal
AU - Aldahan, Ala
AU - El-Askary, Hesham
AU - Ahmed, Alaa
N1 - Publisher Copyright:
© King Abdulaziz University and Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Water is a vital resource essential for all life, and its quality has been compromised by pollution and contamination in recent decades. The Water Quality Index (WQI) is crucial for evaluating water validity for several purposes, including drinking. Accurate WQI prediction allows for proactive strategies to combat water contamination, preserve public well-being, and guarantee access to safe water sources. This study introduces a novel approach utilizing advanced Machine Learning (ML) techniques for WQI prediction, demonstrating substantial improvements over traditional methods. The methods include the Ridge Model, Lasso Model, Random Forest (RF) Model, Extra Trees (ExT) Model, AdaBoost (AB) Model, XGBoost (XGB) Model, Gradient Boosting (GB) Model, LightGBM Model, Linear Regression (LR) Model, K-nearest neighbor (KNN) Model, Regressor (R) Model, Decision Tree (DT) Model, Multi-layer Perceptron (MLP) Model and Support Vector Regressor (SVR) Model, to determine the most effective models for predicting WQI. The proposed models are trained on a publicly available dataset from 145 groundwater well samples collected between January and April 2018 in Abu Dhabi, the United Arab Emirates (UAE). The models’ performance was assessed using various metrics, including Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Adjusted R-squared, Mean Absolute Percentage Error (MAPE) and R-squared (R2). Experimental results indicate promising performance across all models. In particular, the LR Model proved to be exceptionally accurate, precisely predicting WQI values with 100% accuracy during testing. According to the experimental findings, this model surpassed others in regression tasks, achieving an R2 value of 100% in WQI prediction. The proposed research confirms the effectiveness of ML algorithms in the field of Water Resources and will serve as a reference for the researchers working in the field of WQI prediction.
AB - Water is a vital resource essential for all life, and its quality has been compromised by pollution and contamination in recent decades. The Water Quality Index (WQI) is crucial for evaluating water validity for several purposes, including drinking. Accurate WQI prediction allows for proactive strategies to combat water contamination, preserve public well-being, and guarantee access to safe water sources. This study introduces a novel approach utilizing advanced Machine Learning (ML) techniques for WQI prediction, demonstrating substantial improvements over traditional methods. The methods include the Ridge Model, Lasso Model, Random Forest (RF) Model, Extra Trees (ExT) Model, AdaBoost (AB) Model, XGBoost (XGB) Model, Gradient Boosting (GB) Model, LightGBM Model, Linear Regression (LR) Model, K-nearest neighbor (KNN) Model, Regressor (R) Model, Decision Tree (DT) Model, Multi-layer Perceptron (MLP) Model and Support Vector Regressor (SVR) Model, to determine the most effective models for predicting WQI. The proposed models are trained on a publicly available dataset from 145 groundwater well samples collected between January and April 2018 in Abu Dhabi, the United Arab Emirates (UAE). The models’ performance was assessed using various metrics, including Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Adjusted R-squared, Mean Absolute Percentage Error (MAPE) and R-squared (R2). Experimental results indicate promising performance across all models. In particular, the LR Model proved to be exceptionally accurate, precisely predicting WQI values with 100% accuracy during testing. According to the experimental findings, this model surpassed others in regression tasks, achieving an R2 value of 100% in WQI prediction. The proposed research confirms the effectiveness of ML algorithms in the field of Water Resources and will serve as a reference for the researchers working in the field of WQI prediction.
KW - Groundwater Management
KW - Hyper-arid Regions
KW - Machine Learning
KW - Performance Evaluation
KW - Prediction Models
KW - Water Quality Index
UR - http://www.scopus.com/inward/record.url?scp=85210364947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85210364947&partnerID=8YFLogxK
U2 - 10.1007/s41748-024-00524-8
DO - 10.1007/s41748-024-00524-8
M3 - Article
AN - SCOPUS:85210364947
SN - 2509-9426
JO - Earth Systems and Environment
JF - Earth Systems and Environment
ER -