Sürdürülebilir Kalkınma Amaçları
Yayınlar
- ArticleAçık Erişim2025Assessing the efficacy of various predictive models in simulating monthly reference evapotranspiration patterns and its impact on water resource management for agriculture in the Kebir-West watershed, North-East of Algeria
Saci, Rayane; Keblouti, Mehdi; Katipoǧlu, Okan Mert; Durin, Bojan; Majour, Habiba; Sayad, Lamine; Bouzahar, Faiza; Benchaiba, Leila
The estimation of monthly reference evapotranspiration (ETo) is important for the efficient management of water resources, especially in regions with limited meteorological station coverage such as Algeria. For this purpose, different prediction models including support vector machines, multiple regression, bagged trees, and neural networks were applied to estimate Penman-Monteith FAO-56-based monthly ETo in the Oued El Kebir watershed in northeastern Algeria. Eight combinations of climate inputs, including wind speed, relative humidity, and maximum and minimum temperatures, were examined. Four metrics were used to assess the models' performance: coefficient of determination (R²), mean relative error (MRE), mean absolute error (MAE), and root mean square error (RMSE). Sobol sensitivity analysis was conducted to determine the most influential parameter in ETo estimation. According to the results, the variable with the highest impact was maximum temperature. The findings indicate that the proposed models achieved high estimation accuracy. Among them, neural networks outperformed the other models, with a correlation coefficient of 0.99, RMSE = 0.37, MAE = 0.28, and MRE = 0.005, for a period spanning (1984-2022). This superior performance is attributed to their ability to simulate complex and nonlinear relationships between climatic variables and ETo. These results contribute to improved irrigation planning and more efficient water resource management for farmers, climate scientists, and water managers.
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 - ArticleAçık Erişim2024Comparative analysis of data-driven models and signal processing techniques in the monthly maximum daily precipitation prediction of El Kerma station Northeast of Algeria
Katipoğlu, Okan Mert; Keblouti, Mehdi
The availability of a climate database is an essential requirement for modeling and mapping hydrological and environmental processes. Regardless of the nature and structure of these models, most of them require a complete and reliable dataset on a spatiotemporal basis. Unfortunately, the measurement of hydrological variables (precipitation, flow, etc.) can be affected by systematic errors, gaps and random data. The watershed of Seybouse located in the North-East of Algeria, has a network that has gaps in the monthly data with an average error percentage of 20.46% over the period of 1970–2008 on all the stations in operation. The current study combines support vector regression, artificial neural networks, boosted tree, bagged tree, gaussian processes regression and linear regression techniques with empirical mode decomposition and discrete wavelet transform techniques to fill the missing precipitation gaps. This study aims to determine which kernel function, regression type, tree and network structure and data decomposition technique will produce the best outputs in predicting missing rainfall. For this purpose, it aims to comprehensively evaluate various soft computing and signal processing models and use strengthened hybrid approaches by combining them. While modeling, 70% of the data was used for training and the rest for testing. Mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Taylor diagram and Violin plots were used to find the best model. As a result of the analysis, it was concluded that the SVM model produced superior outputs compared to other machine learning models. The single LSVM model had an R2 value of 0.78, RMSE of 8.18, and MAE of 5.90. The W-LSVM model had an R2 value of 0.78, RMSE of 8.10, and MAE of 5.90. The EMD-CGSVM model had an R2 value of 0.69, RMSE of 10.43, and MAE of 7.10. These results indicate that the W-LSVM models provided the most accurate rainfall forecast outputs. The results showed that the wavelet-based linear support vector regression model in precipitation prediction is the most successful artificial intelligence model. Finally, using the W-LSVM model to predict rainfall in the study area can significantly improve water resources management and flood strategies and help optimize water use and structures.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network
Katipoğlu, Okan Mert; Aktürk, Gaye; Kılınç, Hüseyin Çağan; Terzioğlu, Zeynep Özge; Keblouti, Mehdi
Suspended sediment load estimation is vital for the development of river initiatives, water resources management, the ecological health of rivers, determination of the economic life of dams and the quality of water resources. In this study, the potential of Feed Forward Neural Network (FFNN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Frog Leaping Algorithm (SFLA) models was evaluated for suspended sediment load (SSL) estimation in Yeşilırmak River. The heat map of Pearson correlation values of meteorological and hydrological parameters in 1973–2021, which significantly impacted SSL estimation, was examined to estimate SSL values. As a result of the analysis it was developed a prediction model with three different combinations of precipitation, stream flow and past SSL values (M1: streamflow, M2: streamflow and precipitation, M3: streamflow, precipitation, and SSL). The prediction accuracy of the models was visually compared with the Coefficient of Determination (R2), Bias Factor (BF), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Kling-Gupta Efficiency (KGE) statistical criteria and Bland-Altan plot, boxplot, scatter plot and line plot. Based on the analyses, the PSO-ANN model in the M1 model combination showed good estimation performance with an RMSE of 1739.92, MAE of 448.56, AIC of 1061.55, R2 of 0.96, MBE of 448.56, and BF of 0.29. Similarly, the SFLA-ANN model in the M2 model combination had an RMSE of 1819.58, MAE of 520.64, AIC of 1069.9, R2 of 0.96, MBE of 520.64, and BF of 0.19. In the M3 model combination, the SFLA-ANN model achieved an RMSE of 1423.09, MAE of 759.88, AIC of 1071.9, R2 of 0.81, MBE of 411.31, and BF of -0.77. Overall, these models can be considered good estimators as their predicted values are generally close to the measured values. The study outputs can help ensure water structures’ effective lifespan and operation and take precautions against sediment-related disaster risks.
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 künye2024Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction
Katipoğlu, Okan Mert; Mohammadi, Babak; Keblouti, Mehdi
Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Application of novel artificial bee colony optimized ANN and data preprocessing techniques for monthly streamflow estimation
Katipoğlu, Okan Mert; Keblouti, Mehdi; Mohammadi, Babak
Streamflow estimation is important in hydrology, especially in drought and flood-prone areas. Accurate estimation of streamflow values is crucial for the sustainable management of water resources, the development of early warning systems for disasters, and for various applications such as irrigation, hydropower production, dam sizing, and siltation management. This study developed the ANN algorithm by optimizing with an artificial bee colony (ABC). Then, the ABC-ANN hybrid model, which was established, was combined with different signal decomposition techniques to evaluate its performance in streamflow estimation in the East Black Sea Region, Türkiye. For this purpose, the lagged streamflow values were divided into subcomponents using the local mean decomposition (LMD) with the empirical envelope and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) signal decomposition techniques presented to the ABC-ANN algorithm. Thus, the success of the novel hybrid LMD-ABC-ANN and CEEMDAN-ABC-ANN approaches in streamflow prediction was evaluated. The outputs are reliable strategies and resources for water resource planners and policymakers.
RDL Kalıcı Kaydıİncele