Sürdürülebilir Kalkınma Amaçları
Yayınlar
- ArticleYalnız künye2025Irrigation water quality classification using boosting and CNN-BiGRU-Attention models: feature importance assessment using Hellinger distance
Zerouali, Bilel; Derdour, Abdessamed; Almalki, Abdulrahman Seraj; Katipoglu, Okan Mert; Arafat, Ahmed A.; Santos, Celso Augusto Guimarães; Bailek, Nadjem; Hussein, Enas E.
This study addresses a gap in irrigation water quality classification by comparing machine learning (ML) algorithms and deep learning (DL) models, with a focus on feature selection. Feature relevance was assessed using Hellinger distance, mutual information, and Chi-square analysis. Classifiers included boosting algorithms (AdaBoost, XGBoost, CatBoost, LightGBM, GradientBoosting, HistGradientBoosting), Extra Trees, and DL models (CNN-BiGRU and CNN-BiGRU-Attention). Performance was evaluated using standard metrics on 166 samples from Algeria’s Naama region. DL models achieved the highest accuracies (0.82–0.84) and AUC scores (0.98–0.981), followed by XGBoost and CatBoost. Feature analysis identified qiCl as most impactful (Hellinger distance = 0.248), and qiNa as most informative (mutual information= 1.0212). These findings underscore the strength of DL approaches and the value of integrated feature selection. The results offer practical insights for optimizing irrigation strategies, supporting risk assessment, and guiding sustainable water resource management.
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 - ArticleAçık Erişim2023Evaluation of the success of the hybrid wavelet-based ANFIS approach in the estimation of monthly stream flows of the Bitlis River, Turkey
Katipoglu, Okan Mert
This study aimed to predict monthly flows using an adaptive neuro-fuzz y inference system (ANFIS) and wavelet-ANFIS (W-ANFIS) and to deter-mine the effect of wavelet transformation on the success of the machine learning model. For this purpose, the model inputs are divided into three subcomponents with Daubechies 10 mother wavelets. Subcomponents with the highest correlation were chosen as inputs. The most suitable models were selected by dividing the inputs into 3-7 sub-sets, using 11 different lagged input combinations, and testing various membership functions. In establishing the ANFIS model, 75% of the data were used for training and 25% for testing. The performance of ANFIS models was evaluated with root mean square error, Pearson correlation coefficient, determination coefficients, and Taylor diagram. A model with two sub-sets, a hybrid learning algorithm, a Gbellmf membership function, and 400 iterations was selected as the most suit-able. It was concluded that the W-ANFIS model used with the wavelet transform method increased the success of the established ANFIS model. Moreover, it was suggested that the W-ANFIS hybrid machine learning model established in the study can be used effectively in simi-lar climatic regions fed by snowmelt and dominated by a semi-arid climate.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques
Kartal, Veysi; Emiroglu, Muhammet Emin; Katipoglu, Okan Mert; Karakoyun, Erkan
Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863).
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.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2021Spatio-temporal analysis of meteorological and hydrological droughts in the Euphrates Basin, Turkey
Katipoglu, Okan Mert; Acar, Reşat; Şenocak, Serkan
In this study, the aim was to measure changes in the spatio-temporal distribution of a potential drought hazard area and determine the risk status of various meteorological and hydrological droughts by using the kriging, radial basis function (RBF), and inverse distance weighting (IDW) interpolation methods. With that goal, in monthly, three-month, and 12-month time periods drought indices were calculated. Spatio-temporal distributions of the droughts were determined with each drought index for the years in which the most severe droughts were experienced. According to the results, the basin is under risk of meteorological drought due to the occurrence of severe and extreme droughts in most of the area, and especially in the north, during the monthly and three-month time periods. During the 12-month period, it was found that most of the basin is under risk of hydrological drought due to the occurrence of severe and extreme droughts, especially in the southern parts. The most effective interpolation method for the prediction of meteorological and hydrological droughts was determined as kriging according to the results of the cross-validation test. It was concluded that a drought management plan should be made, and early warnings and precautions should be applied in the study area.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2020Comparison of meteorological indices for drought monitoring and evaluating: A case study from euphrates basin, Turkey
Katipoglu, Okan Mert; Acar, Reşat; Şengül, Selim
Drought incidents occur due to the fact that precipitation values are below average for many years. Drought causes serious effects in many sectors, such as agriculture, economy, health, and energy. Therefore, the determination of drought and water scarcity, monitoring, management, and planning of drought and taking early measures are important issues. In order to solve these issues, the advantages and disadvantages of five different meteorological drought indices were compared, and the most effective drought index was determined for monitoring drought. Accordingly, in the monthly, 3-month, and 12-month time period, covering the years between 1966 and 2017 (52 years), Standardized Precipitation Index (SPI), Statistical Z-Score Index (ZSI), Rainfall Anomaly Index (RAI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI) were used. It was concluded that precipitation-based SPI and ZSI are similar patterns and precipitation, and temperature-based SPEI and RDI are similar patterns. Also, it has been determined that RAI is more effective than other indices in determining the periods of extreme drought or wet. Furthermore, SPEI and RDI have been found to be superior to other indices as they take into account the water consumption and climate effects caused by evapotranspiration.
RDL Kalıcı Kaydıİncele