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Öğe A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting(Pergamon-Elsevier Science Ltd, 2024) Katipoglu, Okan Mert; Ertugay, Nese; Elshaboury, Nehal; Akturk, Gaye; Kartal, Veysi; Pande, Chaitanya BaliramDrought 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 shortterm 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.Öğe Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition(Springer Heidelberg, 2024) Elbeltagi, Ahmed; Katipoglu, Okan Mert; Kartal, Veysi; Danandeh Mehr, Ali; Berhail, Sabri; Elsadek, Elsayed AhmedVarious critical applications, spanning from watershed management to agricultural planning and ecological sustainability, hinge upon the accurate prediction of reference evapotranspiration (ETo). In this context, our study aimed to enhance the accuracy of ETo prediction models by combining a variety of signal decomposition techniques with an Artificial Bee Colony (ABC)-artificial neural network (ANN) (codename: ABC-ANN). To this end, historical (1979-2014) daily climate variables, including maximum temperature, minimum temperature, mean temperature, wind speed, relative humidity, solar radiation, and precipitation from four arid and semi-arid regions in Egypt: Al-Qalyubiyah, Cairo, Damietta, and Port Said, were used. Six techniques, namely, Empirical Mode Decomposition, Variational Mode Decomposition, Ensemble Empirical Mode Decomposition, Local Mean Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and Empirical Wavelet Transform were used to evaluate signal decomposition efficiency in ETo prediction. Our results showed that the highest ETo prediction accuracy was obtained with ABC-ANN (Train R-2: 0.990 and Test R-2: 0.989), (Train R-2: 0.986 and Test R-2: 0.986), (Train R-2: 0.991 and Test R-2: 0.989) and (Train R-2: 0.988 and Test R-2: 0.987) for Al-Qalyubiyah, Cairo, Damietta, and Port Said, respectively. The impressive results of our hybrid model attest to its importance as a powerful tool for tackling the problems associated with ETo prediction.Öğe Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria(Mdpi, 2023) Achite, Mohammed; Katipoglu, Okan Mert; Jehanzaib, Muhammad; Elshaboury, Nehal; Kartal, Veysi; Ali, ShoaibDrought 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.Öğe Modeling of irrigation water quality parameter (sodium adsorption ratio) using hybrid swarm intelligence-based neural networks in a semi-arid environment at SMBA dam, Algeria(Springer Wien, 2024) Achite, Mohammed; Katipoglu, Okan Mert; Elshaboury, Nehal; Kartal, Veysi; Akturk, Gaye; Ertugay, NeseSodium 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.Öğe Optimizing river flow rate predictions: integrating cognitive approaches and meteorological insights(Springer, 2024) Kartal, Veysi; Karakoyun, Erkan; Akiner, Muhammed Ernur; Katipoglu, Okan Mert; Kuriqi, AlbanThe models used in this study make it possible to make more accurate predictions about river discharge. These results can influence flood protection strategies, water resource management, and hydropower generation. Due to their ability to capture the underlying temporal relationships in the data, time series forecasts have become increasingly popular in recent years. This study examines the critical processes in river forecasting for the Kizilirmak River basin. We begin with a look at data collection and preparation, followed by an overview of time series forecasting models. Finally, we look at the process of model testing and selection. Seven techniques were used to predict streamflow from meteorological data: Artificial Neural Network (ANN), Firefly-based ANN (FFA-ANN), Random Forest (RF), K-Nearest Neighbors (KNN), Generalized Linear Regression (GLR), Support Vector Machines (SVM), Least Squares Boosted Trees (LSBT). The performance of the models was evaluated using the statistical indicators. The LSBT, RF, and ANN models provided the best results for Kayseri, K & imath;r & scedil;ehir, and Gemerek stations, respectively. The RF, ANN and GLR models provided second best results for these stations, respectively.Öğe Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches(Pergamon-Elsevier Science Ltd, 2024) Kartal, Veysi; Katipoglu, Okan Mert; Karakoyun, Erkan; Simsek, Oguz; Yavuz, Veysel Suleyman; Ariman, SemaThis study aims to predict groundwater drought-based meteorological drought index using machine learning instead of traditional approaches. Groundwater drought (GWD) was predicted using machine learning methodologies such as Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Least Squares Boosting Tree (LSBT), Generalized Linear Regression (GLR) and kNearest Neighbours (KNN). In addition, monthly, seasonal, and annual drought indices such as the Standardised Precipitation-Evapotranspiration Index (SPEI), China Z Index (CZI), Standardised Precipitation Index (SPI), ZScore Index (ZSI), Decile Index (DI), Percent of Normal Index (PNI) and Rainfall Anomaly Index (RAI) were used to analyse the drought of groundwater. These traditional drought indices were modified for the assessment of groundwater drought. Moreover, groundwater drought was predicted based on the hydro-meteorological parameters (temperature, relative humidity, wind speed, rainfall, groundwater level). The applied models' performances were evaluated via Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), R-squared (R2), Mean Bias Error (MBE), Bias Factor, and Variance Account Factor (VAF). Linear SVM is generally the best model for predicting GWD, while GLR is the second-best performing model. The KNN algorithm obtained the weakest performances. Although all types of drought and wet categories were observed, normal drought occurred more than in the other drought and wet categories. This study can contribute to the assessment of groundwater drought in regions where there is no groundwater station.Öğe Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River(Springer, 2024) Katipoglu, Okan Mert; Kartal, Veysi; Pande, Chaitanya BaliramThe service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the & Ccedil;oruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t - 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = - 251.090, Bias Factor = - 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.Öğe Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition(Elsevier, 2024) Ladouali, Sabrina; Katipoglu, Okan Mert; Bahrami, Mehdi; Kartal, Veysi; Sakaa, Bachir; Elshaboury, Nehal; Keblouti, MehdiStudy 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.Öğe Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition (vol 54, 101861, 2024)(Elsevier, 2024) Ladouali, Sabrina; Katipoglu, Okan Mert; Bahrami, Mehdi; Kartal, Veysi; Sakaa, Bachir; Elshaboury, Nehal; Keblouti, Mehdi[Abstract Not Available]Öğe Understanding run theory for evaluating hydrologic drought in the Wadi Mina Basin (Algeria): A historical analysis(Springer Wien, 2024) Achite, Mohammed; Katipoglu, Okan Mert; Jehanzaib, Muhammad; Kartal, Veysi; Mansour, HamidiDrought 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.
