Sürdürülebilir Kalkınma Amaçları
Yayınlar
- 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 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 - ArticleAçık Erişim2025A coupled extreme gradient boosting-MPA approach for estimating daily reference evapotranspiration
Achite, Mohammed; Nasiri, Hamid; Katipoğlu, Okan Mert; Abdallah, Mohammed; Moazenzadeh, Roozbeh; Mohammadi, Babak
Reference evapotranspiration (ET0) modeling is pivotal for irrigation scheduling and water resources planning. This study presents a hybrid approach integrating Extreme Gradient Boosting (XGB) with Marine Predators Algorithm (MPA) for daily ET0 estimation in northern Algeria. The proposed XGB-MPA model was evaluated against traditional empirical models and assessed using statistical methods. Shapley Additive Explanations (SHAP) was employed to enhance model interpretability. Various combinations of meteorological variables were tested as inputs, including air temperature, relative humidity, sunshine hours, wind speed, and extraterrestrial solar radiation. The XGB-MPA hybrid model achieved superior prediction accuracy during testing (R2 = 0.9958, RMSE = 0.1713 mm/day) compared to traditional empirical models and the standard XGB model. The study demonstrated that ET0 prediction accuracy increased with the number of meteorological inputs used. Our findings highlight the XGB-MPA hybrid model's potential for accurate ET0 estimation in northern Algeria, which can be used for water resource management and irrigation planning.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers
Achite, Mohammed; Katipoğlu, Okan Mert; Kartal, Veysi; Sarıgöl, Metin; Jehanzaib, Muhammad; Gül, Enes
The rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effective management and utilization of water resources, as well as for the design of hydraulic infrastructure. Furthermore, research on streamflow estimation has gained heightened importance because water is essential not only for the survival of all living organisms but also for determining the quality of life on Earth. In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network–recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. The performance of each technique was evaluated using statistical criteria such as mean square error (MSE), mean bias error (MBE), mean absolute error (MAE), and the correlation coefficient (R). The results of this study demonstrated that the GMDH algorithm produced the most accurate forecasts at the Sidi AEK Djillali station, with metrics of MSE: 0.132, MAE: 0.185, MBE: −0.008, and R: 0.636. Similarly, the CNN-RNN algorithm achieved the best performance at the Kef Mehboula station, with metrics of MSE: 0.298, MAE: 0.335, MBE: −0.018, and R: 0.597.
RDL Kalıcı Kaydıİncele - 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 - 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 - ArticleAçık Erişim2024Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran)
Saroughi, Mohsen; Mirzania, Ehsan; Achite, Mohammed; Katipoğlu, Okan Mert; Ehteram, Mohammad
Abstract: Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation, and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm’s success. Furthermore, the Shannon entropy of model performance was assessed according to 44 different statistical indicators, classified into two classes: accuracy and error. Hence, based on the high value of Shannon entropy, the best statistical indicator was selected. The results of the best model and the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for the accuracy class than error class. Also, for accuracy and error class, respectively, Akaike information criterion (AIC) and residual sum of squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes, and Legate-McCabe efficiency (LME) and normalized root mean square error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and − 8.3 are the worst indicators of both classes. According to the evaluation best indicator results in the testing phase, the AIC indicator value for HBA-ANN, COOT-ANN, and the standalone ANN models is equal to − 344, − 332.8, and − 175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly improve the performance of the standalone ANN model and offer satisfactory GWL prediction results. Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction. Graphical Abstract: (Figure presented.).
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 Erişim2024Analyzing the dynamical relationships between meteorological and hydrological drought of Wadi Mina basin, Algeria using a novel multiscale framework
Achite, Mohammed; Simsek, Oguz; Sankaran, Adarsh; Katipoğlu, Okan Mert; Caloiero, Tommaso
Understanding the relationship between hydrological and meteorological drought in drought-prone regions is critical for proper reservoir management. This study presents a novel multiscale framework for investigating the associations between hydrological and meteorological drought based on the Time-Dependent Intrinsic Correlation (TDIC) method. Firstly, the characteristics of short, medium and long term standardized precipitation index (SPI) and the standardized runoff index (SRI) of the Wadi Mina basin (Algeria) have been analyzed based on data from 6 rainfall and hydrometric stations. Then an Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise (ICEEMDAN) method is used to decompose the most correlated SPI and SRI series to different scales. A stronger association between the two types of droughts is evident in the low-frequency trend component regardless of the station, but their evolution pattern does not remain the same. Subsequently, a TDIC based running correlation analysis is performed between the modes to examine the SPI–SRI associations over the time domain and across the time scales. TDIC analysis has proven the dynamic behavior in the SPI–SRI associations bearing frequent alterations in nature and strength across the process scales and along the time domain. In general, at the intra-annual scales the SPI–SRI correlations are mostly weak positive with localized alterations to negative along the time domain, whereas the relationship is dominantly strong positive and long range at inter-annual scales up to 4 years. This dynamic behavior in the SPI–SRI association and the evolution pattern of trend decipher that the rainfall processes are not directly transferred to streamflow drought, but it also gets controlled by many other local meteorological processes.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran)
Saroughi, Mohsen; Mirzania, Ehsan; Achite, Mohammed; Katipoğlu, Okan Mert; Al-Ansari, Nadhir; Vishwakarma, Dinesh Kumar; Chung, Il-Moon; Alreshidi, Maha Awjan; Yadav, Krishna Kumar
The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to −25.3%, −29.6% and −57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has −204.9 value for AIC which has grown by 5.23% (−194.7) compared to the state without any pre-processing method (ANN_Relu_25).
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 - ArticleYalnız künye2024Hybrid interpolation approach for estimating the spatial variation of annual precipitation in the Macta basin, Algeria
Achite, Mohammed; Katipoğlu, Okan Mert; Javari, Majid; Caloiero, Tommaso
Spatial precipitation analysis is essential for effectively managing hydrological modeling, construction of water structures, and irrigation planning. In this study, the ordinary kriging (OK), simple kriging (SK), global polynomial interpolation (GPI), local polynomial interpolation (LPI), inverse distance weighted (IDW), radial basis functions (RBF), and artificial neural network (ANN)-based hybrid techniques were compared to determine the spatial variation of annual precipitation. Statistical indicators derived from Willmott’s index of agreement, root mean square error, mean absolute percentage error, and the violin plot and boxplot graphical approaches were used to determine the most effective technique for precipitation interpolation. According to the analysis results, it has been observed that the ANN model significantly improves the prediction performance of single interpolation methods. The OK-ANN hybrid model was determined to be the most accurate representation of precipitation distribution, with the GPI-ANN model coming in second. The most precise results were obtained using the deterministic method, RBF with inverse multiquadric kernel function, LPI with Epanechnikov kernel function, and GPI with 3rd-order polynomial interpolations. In addition, it was determined that deterministic approaches produce more successful results than geostatistical approaches in the basin due to the presence of homogeneous and densely distributed meteorological observation networks.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Understanding run theory for evaluating hydrologic drought in the Wadi Mina Basin (Algeria): A historical analysis
Achite, Mohammed; Katipoğlu, Okan Mert; Jehanzaib, Muhammad; Kartal, Veysi; Mansour, Hamidi
Drought is a natural disaster characterised as precipitation much lower than the precipitation reported in actual periods. Many studies characterized drought as meteorological, hydrological, agricultural, or socioeconomic. When there is a long-term shortage of precipitation, deficits in surface and ground waters occur. In this study, a hydrological drought analysis has been performed for Wadi Mina Basin (4900 km2), which is the biggest sub-basin in Cheliff Basin, using the Streamflow Drought Index (SDI) for the time scales of 3, 6, 9, and 12-month. Monthly mean streamflow records for 05 stations are obtained from the National Water Resources Agency. The obtained SDI values were subjected to Run analysis and drought duration and severity values were calculated. According to the analysis, it has been observed that the maximum (duration: 70 months, severity: 92.78) and average (duration: 31 months, severity: 31.28) droughts occurred at the Sidi AEK Djillali station on a 12-month time scale. The average drought severity was 6.34, with a maximum value of 56.71 on a monthly time scale. However, on a 12-month time scale, the average drought severity increased to 31.28, with a maximum value of 92.78. Therefore, it can be said that the drought severity has increased with the increase in the time scale. When the temporal changes of drought indices are evaluated, it is noteworthy that extraordinary droughts prevailed in the basin in 2000 and 2007. When the scatter diagrams of drought characteristics were examined, it was seen that there was a significant linear relationship between drought duration and severity. In addition, the highest correlation was observed at the 9-month time scale at Ain Hamara (R:0.964) and Oued Abtal (R:0.904) stations. In contrast, the highest correlation was observed at the 12-month time scale at Sidi AEK Djillali (R:0.980) and Takhmaret (R:0.969) stations.
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 künye2023Evaluation of hydro-meteorological drought indices for characterizing historical droughts in the Mediterranean climate of Algeria
Achite, Mohammed; Bazrafshan, Ommolbanin; Katipoğlu, Okan Mert; Azhdari, Zahra
Determining drought indices and characteristics in Algeria is crucial because droughts significantly impact water resources and agricultural production. Additionally, identifying the most suitable drought indicator for the region facilitates effective monitoring of droughts. The study’s main objective is to compare hydro-meteorological droughts, determine their distribution, and assess drought risk. Various drought indices, which are continuous functions of rainfall and other hydro-meteorological variables, are typically used for this purpose. This study calculates seven indices' effectiveness for drought monitoring and assessment in Algeria's Wadi Ouhrane basin. For this purpose, the temporal variation of drought indicators, distribution graphs, weighted Cohen's kappa (Kw), and correlation coefficient values was compared. Among the indices used in the study, four indices, namely SPI, CZI, ZSI, and MCZI, showed high similarity in their behavior. As the time scale increases from 1 to 24 months, the correlation coefficient exceeds 0.97, and Kw becomes greater than 0.57. Furthermore, there is a weak correlation (R < 0.4) between the meteorological and hydrological-based SRI indicators, and the highest correlation was found between the RDI and SRI indices. Therefore, these indices indicate that the precipitation and ET (temperature) ratio is more suitable for hydrological drought studies.
RDL Kalıcı Kaydıİncele