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- ArticleAçık Erişim2026Machine Learning-Based Hydrological Drought Prediction Integrating Teleconnections and Hydrological Memory in a Semi-Arid Basin, Algeria
Katipoğlu, Okan Mert; Achite, Mohammed; Kartal, Veysi; Çelik, Mehmet Ali; Pandey, Kusum
Hydrological drought forecasting in semi-arid basins is challenging due to the combined influence of meteorological forcing, large-scale atmospheric teleconnections, and basin memory processes, which are rarely jointly analysed within a leakage-free predictive framework. This study addresses this gap by evaluating gradient-boosted trees and neural forecasting models for one-month-ahead prediction of the Standardized Runoff Index (SRI) in two sub-basins of the Wadi Sahaouat Basin, Algeria. The models include gradient-boosted regression trees (GBRT), A-N-BEATS, A-N-HiTS, and TiDE, representing distinct forecasting architectures. Predictors consist of the Standardised Precipitation Index (SPI), seven teleconnection indices (NAO, AO, EAWR, SCAND, MEI, SOI, WeMO), and their one- to three-month lags. Two scenarios are tested: Scenario 1 uses SPI and teleconnection lags only, while Scenario 2 additionally includes lagged SRI values (SRI_lag1–3) to represent hydrological memory. A train-only Variance Inflation Factor (VIF > 10) procedure is applied to remove multicollinearity without data leakage. In Basin 1, SRI lags were excluded due to strong collinearity with SPI lags (r = 0.984), resulting in identical inputs for both scenarios. In Basin 2, SRI lags were retained to assess their predictive contribution. GBRT achieved the best overall performance across both basins and scenarios, with mean RMSE, NSE, and KGE values of 0.0682, 0.9907, and 0.8945, respectively. TiDE ranked second overall, with a mean RMSE of 0.1166, followed by A-N-HiTS in third place with a mean RMSE of 0.1203 and A-N-BEATS with the weakest overall performance, with a mean RMSE of 0.2159. These results indicate that gradient-boosted trees remain highly competitive with neural models for small monthly hydrological datasets and that the value of hydrological memory is basin-dependent and varies according to its independence from concurrent meteorological forcing.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Estimation of the pan evaporation coefficient in semi-arid climate conditions via machines learning models
Achite, Mohammed; Katipoğlu, Okan Mert; Vishwakarma, Dinesh Kumar; Pandey, Kusum; Salem, Ali; Elbeltagi, Ahmed
In this study, class A pan coefficient (KPan) values were simulated via five machine learning models, namely the ANN, the AAN-REPTree, the ANN-SMO SVM, the ANN-Linear regression, and the ANN-Bagging model, by using daily meteorological data of the meteorological station of Ouled Ben Abdelkader region, which has semi-arid microclimate in the northwest region of Algeria. To determine the optimal combination of inputs, a variety of input-target pairs were tested by a variety of machine learning models, resulting in seven possible input scenarios: Tmean, RHmin, RHmax and Wind Speed were found to be the best input combinations. The results of models were analyzed (i.e., correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE)) to find their accuracy. As the best optimal condition, four input variables were introduced for the models ANN, ANN-REPTree, ANN-SMO SVM, ANN-Linear regression, and ANN-Bagging, with R = 0.9941 and 0.984, MAE = 0.0018 and 0.0037, RMSE = 0.0068 and 0.0016, RAE = 3.8863 and 7.6858, and RRSE = 10.9301 and 18.0686 in the training and testing phases, respectively. This hybrid model (ANN-Bagging) has demonstrated its utility in a scenario where there is a strong connection among the variables which includes KPan, and its feasibility to display the model in a feasible state.
RDL Kalıcı Kaydıİncele - CorrectionAçık Erişim2026Correction: Estimation of the pan evaporation coefficient in semi-arid climate conditions via machines learning models (Applied Water Science, (2026), 16, 2, (47), 10.1007/s13201-025-02709-6)
Achite, Mohammed; Katipoğlu, Okan Mert; Vishwakarma, Dinesh Kumar; Pandey, Kusum; Salem, Ali; Elbeltagi, Ahmed
In this article, the figures 3 and 5 appeared incorrectly and have now been corrected in the original publication. For completeness and transparency, the old incorrect and correct versions are displayed below: Simple Artificial Neural Networks (ANN) structure Typical architecture of Bagging algorithm The original article has been corrected.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Intercomparison of sediment transport curve and novel deep learning techniques in simulating sediment transport in the Wadi Mina Basin, Algeria
Achite, Mohammed; Katipoğlu, Okan Mert; Elshaboury, Nehal; Tuğrul, Türker; Pandey, Kusum
The accurate estimation of sediment discharge is crucial for the design and operation of engineering structures such as dams, water treatment facilities, and erosion control systems. This study evaluates the performance of various machine learning (ML) and deep learning (DL) models in predicting sediment transport in the Mina Basin, Algeria, at two stations: Oued Abtal and Sidi Abdelkader Djillali. The models include the sediment rating curve, category boosting, convolutional neural network, deep neural network (DNN), gated recurrent unit, and multilayer perceptron. Among these, the DNN model consistently demonstrated superior performance. For Oued Abtal station, the DNN achieved RMSE = 243.72 kg/s, MAE = 102.17 kg/s, NSE = 0.99, and PBIAS = 6.81%. At Sidi Abdelkader Djillali station, it recorded RMSE = 91.27 kg/s, MAE = 46.51 kg/s, NSE = 0.99, and PBIAS = 38.06%. Error analysis revealed that the DNN model offers the most reliable predictions, outperforming both traditional and other ML/DL methods. This study underscores the potential of deep learning models in advancing sediment transport prediction, particularly in semi-arid regions, and highlights their implications for sustainable water resource 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.
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