Sürdürülebilir Kalkınma Amaçları
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- ArticleYalnız künye2026Beyond Conventional Methods: Predicting Novel Modified Groundwater Drought Indices with Advanced Computing Paradigms
Katipoğlu, Okan Mert; Kartal, Veysi; Aktürk, Gaye; Kilinc, Huseyin Cagan; Elshaboury, Nehal; Yamany, Mohamed S.; Pande, Chaitanya Baliram
To achieve accurate and reliable predictions of groundwater drought, it is essential to incorporate relevant meteorological variables and apply advanced predictive methodologies capable of modeling their complex interrelationships. In this study, groundwater drought prediction models were developed based on the seven modified drought indices, which are obtained from groundwater levels and multiple meteorological parameters. The analysis focuses on four districts in Türkiye (Tekirdağ-Çorlu, Tekirdağ-Çerkezköy, Amasya-Merzifon, and Amasya-Centre), which are hereafter referred to collectively as the study areas. Three machine learning models were applied such as a standalone Artificial Neural Network (ANN) and two hybrid models, ANN integrated with Shuffled Frog Leaping Algorithm (SFLA-ANN) and Firefly Algorithm (FFA-ANN). The hybrid metaheuristic algorithms were chosen for their ability to model nonlinear and complex relationships between meteorological variables and groundwater levels, which traditional statistical methods cannot fully capture. Modified drought indices were employed to represent the groundwater drought better. The findings indicate that the hybrid FFA-ANN model generally provides the highest predictive accuracy for most indices, with coefficient of determination values frequently exceeding 0.90. In contrast, the ANN model occasionally outperforms the hybrid FFA-ANN model in terms of root mean square error (RMSE) for specific stations within the study areas. These results highlight the robustness of hybrid ANN approaches, especially FFA-ANN, in predicting groundwater drought under changing climate conditions. The novelty of this study is the modification of meteorological drought indices widely used to represent groundwater drought and the application of hybrid metaheuristic ANN models to estimate these indices in Türkiye. The results demonstrated that such models provide a reliable framework for predicting groundwater drought and provide decision-makers with advanced tools for water resources management, irrigation planning, and climate adaptation strategies.
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 künye2025Advanced forecasting of the reconnaissance drought index in the Aras Basin, Turkey using ABC-optimised ANN with VMD and REMD integration
Mert Katipoğlu, Okan; Achite, Mohammed; Elshaboury, Nehal; Abdelkader, Eslam Mohammed
The Aras Basin in Turkey is highly susceptible to meteorological and hydrological droughts because of the reduced rainfall due to climate change and higher water demand for agriculture. In this context, the main objective of this work was to model the Reconnaissance Drought Index (RDI) over 6- and 12-month periods in two meteorological observation stations in the Aras basin. The prediction performance was evaluated by merging the Artificial Neural Network (ANN) model optimised using the Artificial Bee Colony (ABC) algorithm, namely the ABC-ANN model, with the Variational Mode Decomposition (VMD) and Robust Empirical Mode Decomposition (REMD) methodologies. Some statistical metrics and graphical plots were used to assess the model performance. According to graphical and statistical indicators, the hybrid ABC-ANN (R2 = 0.668-0.967) and VMD-ABC-ANN (R2 = 0.758-0.905) models achieved satisfactory results in estimating RDI values at 6- and 12-month time scales. To evaluate the predictive performance applied models, the Friedman test was employed and revealed statistically significant differences among the models (p < 0.0001). The subsequent Nemenyi post-hoc test identified that ABC-ANN performed significantly better than others. Furthermore, when the temporal variations in forecasts were studied, it was established that the ABC-ANN model was superior to other models in estimating extreme drought values.
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 künye2025Application of Time Series Methodologies for Robust Forecasting of Atmospheric Pollutant Concentrations: PM10, SO2, NO2, NOx, and O3 in an Urban Environment
Katipoğlu, Okan Mert; Elshaboury, Nehal; Kartal, Veysi; Ertugay, Neşe; Kilinc, Huseyin Cagan; Şenocak, Serkan; Pande, Chaitanya Baliram
In recent years, the concentration of air pollutants has risen significantly due to urbanization and increased transportation. Erzincan province, due to its geographical location, is one of the region's most severely impacted by air pollution. Consequently, it is essential to accurately estimate air quality parameters to effectively analyze and manage the associated health risks. This study utilized a time series methodology to examine hourly air quality parameters, including particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitrogen oxides (NOx), and ozone (O3) throughout the year 2022 in Erzincan province. Various predictive techniques, such as feed-forward neural networks, group method of data handling, long short-term memory (LSTM), and least squares boosting tree methods, were employed for the analysis. For estimating air quality parameters, values delayed by up to 4 h—determined as the effective time period through correlation analysis—were utilized. Prediction performance was evaluated using eight different statistical parameters and visualization techniques. Notably, air quality parameters can be effectively estimated using historical data, with the LSTM model demonstrating superior performance compared to other models in this estimation. Furthermore, the most accurate predictions for O3 values were achieved using the LSTM algorithm, yielding an R2 of 0.93 and a root mean square error (RMSE) of 6.02. The findings of this study can aid policymakers in developing water resource management strategies, pollution control policies, and measures to combat climate change.
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 - CorrectionAçık Erişim2024Correction to: Prediction of monthly groundwater level using a new hybrid intelligent approach in the Tabriz plain, Iran (Neural Computing and Applications, (2024), 36, 20, (12609-12624), 10.1007/s00521-024-09681-3)
Mirzania, Ehsan; Achite, Mohammed; Elshaboury, Nehal; Katipoğlu, Okan Mert; Saroughi, Mohsen
In this article Mohsen Saroughi should have been denoted as a second corresponding author. The original article has been corrected.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Prediction of monthly groundwater level using a new hybrid intelligent approach in the Tabriz plain, Iran
Mirzania, Ehsan; Achite, Mohammed; Elshaboury, Nehal; Katipoğlu, Okan Mert; Saroughi, Mohsen
Predicting the groundwater level (GWL) is essential in water resource management and irrigation planning in arid and semi-arid areas. In this study, an artificial neural network (ANN) was combined with newly developed wild horse optimizer (WHO) and egret swarm optimization algorithm (ESOA) techniques to predict a one month lead-time GWL in the Tabriz plain of Iran. For the prediction of the GWL, the number of months and years, the one month lag of average temperature, evaporation, precipitation, and GWL were used as inputs. Model performances were compared using root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and relative strength ratio (RSR) statistical indicators and scatter diagrams, time series graph, violin graph, and Taylor diagram. As a result of the analysis, the most successful estimation results were obtained with the input combinations of year, month, average temperature, evaporation, precipitation, and GWL (t − 1) for the prediction of the one month lead-time GWL. According to the results of evaluation indicators in the testing phase, ANN with (R2 = 0.871, RMSE = 0.306 (m), NSE = 0.832, and RSR = 0.410), WHO–ANN (R2 = 0.932, RMSE = 0.200 (m), NSE = 0.929, and RSR = 0.267), and ESOA–ANN (R2 = 0.952, RMSE = 0.164 (m), NSE = 0.951, and RSR = 0.220). In addition, it was revealed that the ESOA–ANN hybrid model showed higher prediction success than the WHO–ANN and standalone ANN models. The study outputs contribute to decision-makers and planners for controlling land subsidence, assessing GWL and aquifer compaction, irrigation planning, and effective management of water resources.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition
Ladouali, Sabrina; Katipoğlu, Okan Mert; Bahrami, Mehdi; Kartal, Veysi; Sakaa, Bachir; Elshaboury, Nehal; Keblouti, Mehdi; Chaffai, Hicham; Ali, Salem; Pande, Chaitanya B.
Study region: Six regions in Algeria have been selected as follows: Ain Elhadjel, Msaad, Boussaada, Elkantara, M'sila and M'doukel. Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and the Extreme Learning Machine (ELM) algorithm as a preprocessing technique for predicting future droughts. The first 6 and 12-month SPI values 1, 2, and 3-month lead time values were estimated with the ELM algorithm. After that, meteorological variables and Standard Precipitation Index (SPI) values, divided into subcomponents with VMD, are presented to the ELM model, and a drought forecasting model is developed. Model performances were evaluated according to various visual and statistical criteria. New hydrological insights for the region: Soft computing techniques have become the preferred method for producing predictions due to their ability to minimize development time, require minimal input, and offer a relatively less complex approach when compared to dynamic or physical models. As a result of the analysis, it has been determined that the highest prediction accuracies are generally obtained in VMD-ELM models and SPI predictions with a 1-month lead time. The study outputs give important ideas to mite donors regarding water resource planning and climate change adaptation strategies in the study area and can be applied to other arid and semi-arid environments.
RDL Kalıcı Kaydıİncele - CorrectionAçık Erişim2024Corrigendum to “Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition” [J. Hydrol. Reg. Stud. 54 (2024) 101861] (Journal of Hydrology: Regional Studies (2024) 54, (S221458182400209X), (10.1016/j.ejrh.2024.101861))
Ladouali, Sabrina; Katipoğlu, Okan Mert; Bahrami, Mehdi; Kartal, Veysi; Sakaa, Bachir; Elshaboury, Nehal; Keblouti, Mehdi; Chaffai, Hicham; Salem, Ali; Pande, Chaitanya B.
The authors regret the need to issue this corrigendum to acknowledge the change in author name and address for correspondence. 1- The author's name “Salem Ali” should be “Ali Salem” 2- Corresponding author at: Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs, Hungary We thank the journal for accommodating this update. The authors would like to apologise for any inconvenience caused.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Modeling of irrigation water quality parameter (sodium adsorption ratio) using hybrid swarm intelligence-based neural networks in a semi-arid environment at SMBA dam, Algeria
Achite, Mohammed; Katipoğlu, Okan Mert; Elshaboury, Nehal; Kartal, Veysi; Aktürk, Gaye; Ertugay, Neşe
Sodium adsorption rate (SAR), which significantly affects soil and plant health, is determined according to the concentration of sodium ions, calcium, and magnesium in irrigation water. Accurate estimation of SAR values is vital for agricultural production and irrigation. In this study, hybrid swarm intelligence-based neural networks are used to model sodium adsorption ratio in irrigation water quality parameters in a semi-arid environment at Sidi M’Hamed Ben Aouda (SMBA) dam, Algeria. For this, the nature-inspired optimization techniques of particle swarm optimization (PSO), genetic algorithm (GA), Jaya algorithm (JA), artificial bee colony (ABC), and firefly algorithm (FFA) and the signal processing technique of variational mode decomposition (VMD) have been combined with artificial neural networks (ANN). Correlation matrices were used to select the data entry structure in the established models. Water quality parameters with a statistically significant and medium to high relationship with SAR values were presented as input to the model. The overall performance was measured using various statistical metrics, scatter diagrams, Taylor diagrams, correlograms, boxplots, and line plots. In addition, the effect of input parameters on model estimation was evaluated according to Sobol sensitivity analysis. As a result, the GA-ANN algorithm demonstrated superior performance (MSE = 0.073, MAE = 0.193, MAPE = 0.048, MBE=-0.16, R2 = 0.934, WI = 0.968, and KGE = 0.866) based on the statistical indicators, indicating better results compared to other models. The second-best model, ABC-ANN (MSE = 0.084, MAE = 0.233, MAPE = 0.066, MBE=-0.135, R2 = 0.897, WI = 0.965, and KGE = 0.920) was also selected. The weakest prediction outputs were obtained from the VMD-ANN model. The accurate and reliable estimation of SAR in irrigation water has the potential to facilitate improvements in agricultural irrigation management and agricultural production efficiency for farmers, agricultural practitioners, and policymakers.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting
Katipoğlu, Okan Mert; Ertugay, Neşe; Elshaboury, Nehal; Aktürk, Gaye; Kartal, Veysi; Pande, Chaitanya Baliram
Drought is one of the costliest natural disasters worldwide and weakens countries economically by causing negative impacts on hydropower and agricultural production. Therefore, it is necessary to create drought risk management plans by monitoring and predicting droughts. Various drought indicators have been developed to monitor droughts. This study aims to forecast Streamflow Drought Index (SDI) values with various novel metaheuristic optimization-based Artificial Neural Network (ANN) and deep learning models to predict 1-month lead-time hydrological droughts on 1, 3, and 12-month time scales in the Konya closed basin, one of the driest basins in Turkey. To achieve this goal, the ANN model was integrated with the Firefly Algorithm (FFA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) techniques and compared against long short-term memory (LSTM) networks. While establishing the SDI prediction model, lag values exceeding the 95% confidence intervals in the partial autocorrelation function graphs were used. Model performance was evaluated according to scatter matrix, radar, time series, bee swarm graphs, and statistical performance metrics. As a result of the analysis, the PSO-ANN hybrid model with (R2:0.468–0.931) at station 1611 and the FFA-ANN hybrid model with (R2:0.443–0.916) at station 1612 generally have the highest accuracy.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria
Achite, Mohammed; Katipoğlu, Okan Mert; Jehanzaib, Muhammad; Elshaboury, Nehal; Kartal, Veysi; Ali, Shoaib
Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM’s fast-learning ability and discrete wavelet transform’s ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Modeling of meteorological, agricultural, and hydrological droughts in semi-arid environments with various machine learning and discrete wavelet transform
Achite, Mohammed; Katipoglu, Okan Mert; Şenocak, Serkan; Elshaboury, Nehal; Bazrafshan, Ommolbanin; Dalkılıç, Hüseyin Yıldırım
Recent meteorological, hydrological, and agricultural droughts in the Mediterranean regions have raised concerns about the impact of climate change. In this study, the meteorological, agricultural, and hydrological droughts were modeled in the Wadi Ouahrane Basin using various machine learning (ML) models and standardized indices of rainfall, evapotranspiration, and runoff (SPI, SPEI, and SRI) at different times scales (1, 3, 6, 9, 12, and 24 months). The applied ML models were the linear support vector machine (SVM), quadratic SVM, cubic SVM, fine gaussian SVM, medium gaussian SVM, coarse Gaussian SVM, rational quadratic Gaussian process regression (GPR), squared exponential GPR, Matern 5/2 GPR, exponential GPR, bagged tree, and boosted tree. Moreover, the hybrid models acquired by combining these ML models with wavelet transform were evaluated. The performance of the models was analyzed using statistical criteria such as root mean square error, determination coefficient, and mean absolute error. As a result, wavelet-GPR models showed the most promising results in estimating SPI, SPEI, and SRI values. The values for SPI (R 2 of train: 0.393; and test: 0.351), SPEI (R 2 of train: 0.809; test: 0.746), and SRI (R 2 of train: 0.999; test: 0.808) indicate monthly time scale. Additionally, for the time periods of 3, 6, 9, 12, and 24 months, the predictions for SPI, SPEI, and SRI generally obtain R 2 of train 0.99 and test 0.95 values. Moreover, it was determined that wavelet-based ML models, established with inputs divided into three subcomponents with Daubechies mother wavelet, showed superior results than standalone ML models. The study results could guide decision-makers and planners in developing drought risk management and mitigation strategies.
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