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Khoshnam, Mohammad, Karami, Hojat, Shams, Amin, Kisi, Ozgur. (1406). Evaluating Effective AI Models for Surface and Groundwater Quality Prediction: A Review. پژوهشنامه مدیریت اجرایی, (), -. doi: 10.22080/ceas.2026.32582.1127
Mohammad Khoshnam; Hojat Karami; Amin Shams; Ozgur Kisi. "Evaluating Effective AI Models for Surface and Groundwater Quality Prediction: A Review". پژوهشنامه مدیریت اجرایی, , , 1406, -. doi: 10.22080/ceas.2026.32582.1127
Khoshnam, Mohammad, Karami, Hojat, Shams, Amin, Kisi, Ozgur. (1406). 'Evaluating Effective AI Models for Surface and Groundwater Quality Prediction: A Review', پژوهشنامه مدیریت اجرایی, (), pp. -. doi: 10.22080/ceas.2026.32582.1127
Khoshnam, Mohammad, Karami, Hojat, Shams, Amin, Kisi, Ozgur. Evaluating Effective AI Models for Surface and Groundwater Quality Prediction: A Review. پژوهشنامه مدیریت اجرایی, 1406; (): -. doi: 10.22080/ceas.2026.32582.1127

Evaluating Effective AI Models for Surface and Groundwater Quality Prediction: A Review

Civil Engineering and Applied Solutions
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 10 تیر 1406
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/ceas.2026.32582.1127
نویسندگان
Mohammad Khoshnam1؛ Hojat Karami1؛ Amin Shams* 1؛ Ozgur Kisi2، 3، 4
1Faculty of Civil Engineering, Semnan University, Semnan, Iran
2Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany
3Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia
4School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, South Korea
تاریخ دریافت: 12 مرداد 1405،  تاریخ بازنگری: 27 شهریور 1405،  تاریخ پذیرش: 08 مهر 1405 
چکیده
Artificial intelligence (AI) techniques have become important tools for predicting and assessing water quality because they can capture nonlinear relationships among complex environmental variables. This review systematically analyzes AI-based approaches for surface water and groundwater quality prediction and identifies factors that influence model performance. Following PRISMA guidelines, we conducted a systematic review using five scientific databases, screening 224 records and including 35 peer-reviewed studies. We analyzed the reviewed studies by AI model type, water resource category, input parameters, dataset characteristics, hydrological conditions, and prediction performance. The results indicate that hybrid and ensemble models generally outperform standalone algorithms by improving feature extraction, reducing uncertainty, and better capturing nonlinear relationships. GPR showed high accuracy for WQI prediction with uncertainty below 2%, RF-SVM achieved strong performance for BOD prediction (R² = 0.908), and LSTM-based and metaheuristic models improved DO prediction. Despite their high accuracy, AI models still face challenges related to interpretability, data availability, and regional transferability. This review compares AI model suitability under different hydrological conditions and proposes future directions in explainable AI, uncertainty quantification, and adaptive real-time water quality monitoring.
کلیدواژه‌ها
Artificial intelligence؛ Water quality؛ Surface water؛ Groundwater؛ Water management؛ Hydro-informatics؛ Sustainability
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