Sürdürülebilir Kalkınma Amaçları
Yayınlar
- Conference ObjectYalnız künye2025Modeling of Meteorological Drought Under Climate Variability Using the Standardizes Precipitation Evaporation Index (SPEI) and Machines Learning Models for Wadi Ouahrane Basin, Algeria
Achite, Mohammed; Emami, Somayeh; Katipoğlu, Okan Mert; Toubal, Abderrezak Kamel; Zhang, Z.; Chenchouni, H.; Sarikaya, M.A.; Ciner, A.; Knight, J.; Gentilucci, M.
Machine learning algorithms are an important tool in capturing complex relationships and modeling high-dimensional climate data and drought severities. The main innovation of this study is the evaluation of the accuracy and applicability of augmented tree and kernel-based machine learning algorithms in standardized precipitation evapotranspiration index (SPEI) prediction. Rainfall and temperature data obtained from the ONM Department of Chlef City in the Wadi Ouahrane basin (270 km2), from 1973 to 2017 were employed to predict SPEI at a monthly time scale. In the model’s setup, two different models, Gradient Boosted Trees (GBT) and Support vector machine (SVM), were employed. The determination coefficients (R2), root mean square error (RMSE), mean absolute error (MAE) were employed to evaluate the model performances. In terms of accuracy and efficiency, the SVM model (R2 = 0.957, RMSE = 0.216, and MAE = 0.142) exhibits superiority over the GBT (R2 = 0.870, RMSE = 0.369, and MAE = 0.293) for SPEI estimation. Therefore, SVM model has been proposed for drought prediction at various time scales. The results of the study contribute to the understanding of drought dynamics and decision-making by policy makers in water resources management.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Investigating the Performance of Machine Learning Models in Estimating Monthly Dam Evaporation: A Case Study of Sidi Mhamed Ben Aouda Dam, Wadi Mina Basin, Algeria
Achite, Mohammed; Emami, Somayeh; Emami, Hojjat; Katipoğlu, Okan Mert; Pandey, Kusum; Hajimirzajan, Amir; Elshaboury, Nehal
Effective water resource management in arid and semi-arid regions requires accurate estimation of hydrological parameters such as evaporation. This study investigates the monthly evaporation of the Sidi-M’Hamed Ben Aouda dam basin in northwest Algeria using six machine learning models: random forest (RF), extra tree (ET), gradient boosting (GB), category boosting (CatBoost), light gradient boosting machine (LGBM), and multi-layer perceptron (MLP). Climatic inputs (temperature, relative humidity, wind speed, and sunshine hours) from 1978 to 2023 were used to train and test the models. The findings reveal that the ET model achieved the best balance between accuracy and computational speed, while the RF model provided the highest overall accuracy. GB had a faster runtime with slightly reduced accuracy, whereas CatBoost and MLP underperformed. This comparative analysis highlights the suitability of ensemble tree-based models, particularly RF and ET, for accurate and efficient evaporation prediction, supporting water resource planning in data-scarce and climate-sensitive regions.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Performance enhancement of daily reservoir evaporation rate estimation models using stacking regression by discretization with AI methods
Achite, Mohammed; Katipoglu, Okan Mert; Elbeltagi, Ahmed; Elshaboury, Nehal; Pandey, Kusum; Emami, Somayeh; Yongguo, Chen
Accurate estimation of evaporation from surface water bodies is essential for sustainable water resource planning and operational hydrology. However, direct measurement over reservoirs is complex and often impractical. In this study, an advanced machine learning framework based on Regression by Discretization (RD) was applied to predict daily reservoir evaporation at the Sidi-M’Hamed Ben Aouda Dam Basin in Algeria from 2010 to 2023. Among the models evaluated, the RD-Bagging model demonstrated superior performance with a correlation coefficient (CC) of 0.7849, mean absolute error (MAE) of 0.0139, root mean square error (RMSE) of 0.0172, and percent bias (PBIAS) of -1.42% during the testing period. For the validation period, RD-Bagging yielded CC, MAE, RMSE, and PBIAS values of 0.822, 0.0119, 0.0154, and − 1.18%, respectively, indicating high predictive accuracy and low bias. The results confirm the robustness and generalization ability of the RD-Bagging model, making it a reliable tool for reservoir evaporation prediction. This study provides a foundation for the development of data-driven decision support systems in hydrological modeling and climate resilience planning.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024An election algorithm combined with support vector regression for estimating hydrological drought
Achite, Mohammed; Emami, Somayeh; Jehanzaib, Muhammad; Katipoğlu, Okan Mert; Emami, Hojjat
The prediction of hydrological drought is significant for the management of water resources, planning of hydroelectricity production, agricultural production, habitat, and life of living things. The primary aim of this study is to increase the prediction success hydrological drought in the Wadi Ouahrane basin (270 km2). For this purpose, support vector regression technique is hybridized with Election Algorithm. Standardized Runoff Index (SRI) values were used to determine hydrological droughts. In calculating droughts, rainfall and stream flow data covering the years 1972–2018 were employed. Standardized Precipitation Index (SPI) and previous SRI values were used in the establishment of the hydrological drought prediction model. The coefficient of determination, Standard deviation, the Akaike information criterion and root-mean-square error values were used to test the model accuracy. It was identified to be the most accurate according to the research outputs were obtained with SRI-12 and the Election Algorithm-based support vector regression algorithm showed successful results in hydrological drought prediction. In addition, it was revealed that realistic results are obtained when SPI value and delayed SRI values were used as inputs in multi-time scale analysis of hydrological drought.
RDL Kalıcı Kaydıİncele