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    Development of Divergence and Interdependence-based Hybrid Weighting Scheme (DIHWS) for accurate assessment of regional drought
    (Springer Wien, 2024) Mukhtar, Alina; Ali, Zulfiqar; Kartal, Veysi; Karakoyun, Erkan; Yousaf, Mahrukh; Sammen, Saad Sh.
    Accurate ensembles of precipitation data play an important role in precise and efficient drought monitoring systems at the regional level. This article proposes a weighted aggregation scheme - the Divergence and Interdependence-based Hybrid Weighting Scheme (DIHWS) - to ensemble precipitation data for accurate regional drought analysis. The derivation of weights is based on the interdependence among meteorological observatories and the divergence from the mean characteristics of regional data. Here, the interdependence among meteorological observatories is assessed using the Bayesian Network theory. At the same time, the divergence from the mean characteristics of regional data is based on the set of equations used for regional aggregation in (Ali et al., Water Resour Manage 36:4099-4114, 2022). Consequently, the paper introduces a new regional drought index - the Bayesian Network-based Adaptive Regional Drought Index (BNARDI). BNARDI is a standardized regional index and used estimated at multiple time scales. The application of DIHWS and BNARDI is based on five regions of varying observatories. We observed smaller MAE values associated with DIHWS than its Simple Model Average (SMA) and one other of its relevant compitator in all the regions. Therefore, we conclude that the proposed weighting scheme and drought index are more reliable for regional drought monitoring and forecasting. Additionally, the research includes various forecasting models to assess their appropriateness for forecasting the new regional index. The results of this research demonstrate that no single method is suitable for forecasting complex drought data, as generated by BNARDI. Therefore, we suggest using varying methods or a hybrid of various candidate forecasting models for forecasting BNARDI.
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    Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation
    (Springer Heidelberg, 2024) Akiner, Muhammed Ernur; Kartal, Veysi; Guzeler, Anil Can; Karakoyun, Erkan
    The Ye & scedil;il & imath;rmak River Basin in northern T & uuml;rkiye is crucial for the region's water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Ye & scedil;il & imath;rmak River basin (R2 = 0.98, RMSE = 0.18). The study's findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.
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    Optimizing river flow rate predictions: integrating cognitive approaches and meteorological insights
    (Springer, 2024) Kartal, Veysi; Karakoyun, Erkan; Akiner, Muhammed Ernur; Katipoglu, Okan Mert; Kuriqi, Alban
    The 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.
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    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, Sema
    This 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.