Yazar "Vaheddoost, Babak" seçeneğine göre listele

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    Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Turkiye
    (Mdpi, 2023) Gul, Enes; Staiou, Efthymia; Safari, Mir Jafar Sadegh; Vaheddoost, Babak
    The impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Turkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R-2) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Turkiye.
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    Studying the Changes in the Hydro-Meteorological Components of Water Budget in Lake Urmia
    (Amer Geophysical Union, 2022) Vaheddoost, Babak; Fathian, Farshad; Gul, Enes; Safari, Mir Jafar Sadegh
    Abrupt changes in the Lake Urmia water level have been addressed in many studies, and yet the link between the water level decline and hydro-meteorological variables in the basin is a major topic for debate between researchers. In this study, a set of data-driven techniques is used to investigate the components of the water budget in Lake Urmia. Then, the rate of monthly depth differences (DD), precipitation (P), evaporation (E), potential groundwater head (G), and streamflow (Q) time series between 1974 and 2014 are used in the analysis. Several scenarios and strategies are developed by considering the major changes in the year-2000, which is believed to be the initiation of the hydrological encroachment in the basin. Simple water budget (WB), dynamic regression (DR), and symbolic regression (SR) techniques are used to simulate the DD with consideration to P, E, G, and Q. Alternatively, the effect of the year 1997 as the potential base-line for the initiation of significant meteorological trends in the basin is investigated. Conducted analysis showed that the DR models of an autoregressive moving average together with multiple exogenous inputs provide an approximate R-2: 0.7 as the best alternative among the selected models. It is shown that the Q and G depict abrupt changes compared to the P and E, while either the year 1997 (climate effect) or the year 2000 (encroachment effect) is considered as the baseline in the study.
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    Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm
    (Springer Wien, 2021) Sales, Ali Kozekalani; Gul, Enes; Safari, Mir Jafar Sadegh; Ghodrat Gharehbagh, Hadi; Vaheddoost, Babak
    Lake water level changes are relatively sensitive to the climate-born events that rely on numerous phenomena, e.g., surface soil type, adjacent groundwater discharge, and hydrogeological situations. By incorporating the streamflow, groundwater, evaporation, and precipitation parameters into the models, Urmia lake water depth is simulated in the current study. For this, 40 years of streamflow and groundwater recorded data, respectively collected from 18 and 9 stations, are utilized together with evaporation and precipitation data from 7 meteorological stations. Extreme learning machine (ELM) is hybridized with four different optimizers, namely artificial bee colony (ABC), ant colony optimization for continuous domains (ACOR), whale optimization algorithm (WOA), and improved grey wolf optimizer (IGWO). In the analysis, 13 various scenarios with multiple input combinations are used to train and test the employed models. The best scenarios are then opted based on the performance metrics which are applied to assess the accuracy of the methods. According to the results, the hybrid ELM-IGWO shows better performance compared to the ELM-ABC, ELM-ACOR, and ELM-WOA approaches. Results indicate that the groundwater and persistence of the lake water depth have effective roles in models while incorporating higher number of variables can lower the performance of the models. Statistical analysis showed a 62% improvement in the performance of ELM-IGWO in comparison to the ELM-WOA with regard to the root mean square error. The promising outcomes obtained in this study may encourage the application of the utilized algorithms for modeling alternative hydrological problems.