Yazar "Safari, Mir Jafar Sadegh" seçeneğine göre listele
Listeleniyor 1 - 20 / 27
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A CMIP6-based drought assessment over Küçük Menderes Basin,Türkiye(Springer Wien, 2025) Rotbeei, Farzad; Nuri Balov, Mustafa; Safari, Mir Jafar Sadegh; Vaheddoost, BabakDroughts are the phenomenon of which their magnitude and frequency are forecasted to escalate over time primarily due to the impacts of climate change and global warming. Hence, the potential consequences of the expected drought events are of the great importance in performing effective adaptation and regional mitigation strategies. The objective of the current study is to explore the consequences of climate change on the future droughts in K & uuml;& ccedil;& uuml;k Menderes Basin in western T & uuml;rkiye. This objective will be addressed by examining the outputs of four General Circulation Models (GCMs) incorporated within Phase 6 of the Coupled Model Inter-comparison Project (CMIP6), with particular emphasis on two contrasting emission trajectories: SSP2-4.5 and SSP5-8.5. The daily precipitation and temperature projections are then utilized in determination of the so-called Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) drought indices with consideration to 2015-2039 as near future, 2040-2069 as mid-term future, and 2070-2099 as late future time frames. According to projections based on the SSP2-4.5 and SSP5-8.5 scenarios, the number of dry months is anticipated to escalate by approximately 26.12% and 39.80%, respectively, toward the end of the twenty-first century (2070-2099), in contrast to the reference period (1985-2014). Results of the current study provide valuable insights for developing adaptation strategies to address future consequences of drought events in the K & uuml;& ccedil;& uuml;k Menderes Basin amid evolving climate conditions.Öğe A Joint Evaluation of Streamflow Drought and Standard Precipitation Indices in Aegean Region, Turkey(Springer Basel Ag, 2023) Gulmez, Ayse; Mersin, Denizhan; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Tayfur, GokmenWater is an invaluable substance that ensures the life cycle and causes hydrologic events worldwide. Water deficit, also known as drought, is a naturally occurring disaster that affects the hydrometeorologic and/or climatic responses in time and space. In this study, the meteorologic and hydrologic droughts in Buyuk Menderes, Kucuk Menderes, and Gediz basins in Turkey are investigated. The streamflow drought index (SDI) and standard precipitation index (SPI) are used considering different time windows. To achieve this, the monthly streamflow at Cicekli-Nif, Besdegirmenler-Dandalas, Bebekler-Rahmanlar, and Kocarli-Koprubasi hydrometric stations together with monthly precipitation at 14 meteorologic stations during 1973-2020 (47 years) are used. The SDI and SPI with 1, 3, 6, and 12 months moving average are then used to express the association between the meteorologic and hydrologic droughts in the basin. Results showed that the SDI depicts no abnormal situations, while the SPI rates in the 1980s and 2010s indicated severe droughts. It was concluded that the inner parts of the basins are prone to frequent droughts, and there is a concordance between SPI and SDI patterns at the basin level. However, minor discrepancies between SPI and SDI do exist and probably originated from temporal delays and water abstraction.Öğe A multi-step strategy for enhancing the rainfall-runoff modeling: combination of lumped and artificial intelligence-based hydrological models(Springer, 2025) Mohammadi, Babak; Safari, Mir Jafar Sadegh; Vaheddoost, Babak; Yilmaz, Mustafa UtkuAccurate rainfall-runoff (RR) modeling holds significant importance in environmental management, playing a central role in understanding the dynamics of water cycle. In this respect, the precision in the determination of RR is crucial for mitigating the adverse effects of both water scarcity and excessive runoff, ensuring the sustainable management of ecosystems and water resources. As a primary hydrological variable, runoff engages in direct interactions with other hydrological variables. Due to the complexity of the RR process, two primary approaches are commonly used in modeling, namely conceptual (lumped) models and artificial intelligence (AI) models. Conceptual approaches are based on hydrological processes and use a larger number of hydrological variables, yet they often exhibit lower performance compared to AI models. In contrast, AI models rely on fewer parameters and lack physical interpretability, but demonstrate high performance. This study merges the advantages of both lumped and AI techniques to develop an advanced RR model. Hence, the applicability of several lumped and AI-based models in estimating the streamflow rates with the help of basic meteorological variables is investigated. The lumped hydrological models, namely the Modello Idrologico SemiDistribuito in continuo (MISD), Identification of Unit Hydrographs and Component Flows from Rainfall, Evaporation, and Streamflow (IHACRES), and G & eacute;nie Rural & agrave; 4 param & egrave;tres Journalier (GR4J), are employed in conjunction with AI algorithms as Radial Basis Function (RBF) neural networks, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP). An ensemble of conceptual models (MISD, IHACRES, and GR4J) and three AI models (MLP, RBF, and ANFIS) with various lag times are considered as effective variables, where Support Vector Machine (SVM) was utilized as a feature selection method with five different kernels in determining the best inputs. Afterward, the SVM-ANFIS model, as the best model, is hybridized with Ant Colony Optimization (ACO) to develop the SVM-ANFIS-ACO model. It is found that the coupling of lumped and AI methodologies considerably enhanced the accuracy of the RR models; and SVM-ANFIS-ACO outperformed other models in streamflow computation.Öğe A stochastic approach for the assessment of suspended sediment concentration at the Upper Rhone River basin, Switzerland(Springer, 2022) Vaheddoost, Babak; Vazifehkhah, Saeed; Safari, Mir Jafar SadeghThis study addresses the link between suspended sediment concentration, precipitation, streamflow, and direct runoff components. This is important since suspended sediment concentration in the streamflow has invaluable importance in the management of the river basin. For this, the daily streamflow time series in five consecutive stations at Upper Rhone River Basin, a relatively large basin in the Alpine region of Switzerland, daily precipitation at one station, and the twice a week suspended sediment concentration records at the most downstream station between January 1981 and October 2020 are used. Initially, the base flow and the direct runoff associated with streamflow time series are obtained using the sliding interval method. Elasticity analyses between streamflow and suspended sediment concentration together with correlation, autocorrelation, partial autocorrelation, stationarity, and homogeneity are examined by the Augmented Dickey-Fuller and Pettitt's tests, respectively. Then, various stochastic scenarios are generated using the autoregressive moving average exogenous method (ARMAX). It is concluded that the precipitation and direct runoff have fewer effects on the suspended sediment concentration at downstream of the river. Hence, the cumulative effect of the glacier or snowmelt and channel erosion may exceed the effect of rain blown washouts on the suspended sediment concentration at the Port du Scex station. It is found that the ARMAX model results are satisfactory and can be suggested for further application.Öğe Assessment of Drought in Izmir District Using Standardized Precipitation Index(Springer Nature, 2025) Mersin, Denizhan; Gulmez, Ayse; Safari, Mir Jafar Sadegh; Vaheddoost, Babak; Tayfur, GökmenOne of the main issues with agro-food and socio-economical security in the world is droughts. Regardless of cause or effect, the ever-changing climate is placing increasing strain on water resources pushing supply to its limits. Izmir, a growing city in Turkey, is endowed with variety of water resources, such as lakes, rivers, seashores, and groundwater reserves. Therefore, it is crucial for the planning and development of the area to examine past and foreseeable drought occurrences and their possible impact on water resources. In this regard, the study’s goal is to assess historical droughts in Izmir District. Data from three meteorological stations in Küçük Menderes basin, collected between 1973 and 2020, are utilized in this study. To establish the validity of the posterior drought analysis, the consistency and trend in the time series are first examined using the double mass curve, run test, and linear trend analysis. The next step is to assess the historical deficit related to meteorological, agricultural, and hydrological droughts using the SPI and moving mean (MA) operator. The temporal analysis of SPI reveals distinct drought patterns across the stations, with multiple moderate to extreme droughts occurring particularly between 1998 and 2010, highlighting significant spatial and temporal variability in drought severity and frequency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Öğe Comparability Analyses of Three Meteorological Drought Indices in Turkey(CRC Press, 2023) Vaheddoost, Babak; Safari, Mir Jafar SadeghThe following chapter investigates the role of precipitation in the evaluation of meteorological drought in a mountainous region. For this, Mount Uludag in Turkey was taken as the case of study. Three meteorological stations with quite long precipitation records were used. Monthly precipitation time series between January 1980 and October 2018 at the Keles and Osmangazi stations in the northern and southern hillsides, together with the Uludag station near the summit were used in the analysis. Afterward, the patterns in the data run, frequency changes, and temporal events related to the time series were evaluated using precipitation anomaly, z-index, autocorrelation, mutual information, and power spectrum. It was concluded that there is a strong seasonality in the data at every 6 and 12 months, whereas the temporal persistence is quite low and decays after the second time lag. In the next stage, three drought indices, namely the Standardized Precipitation Index (SPI), Deciles Index (DI), and percent of normal (PN) were calculated at monthly, seasonal, and annual scales for each station. Finally, a model based on the spatial, temporal, and spatiotemporal properties of the precipitation time series was developed using the multivariate adaptive regression splines (MARS) model. It was concluded that the spatial scenario is the best predictive model in the assessment of precipitation and drought, and the SPI is the best one-parameter meteorological drought index for use in drought studies. © 2024 Taylor & Francis Group, LLC.Öğe Data Reconstruction for Groundwater Wells Proximal to Lakes: A Quantitative Assessment for Hydrological Data Imputation(Mdpi, 2025) Can, Murat; Vaheddoost, Babak; Safari, Mir Jafar SadeghThe reconstruction of missing groundwater level data is of great importance in hydrogeological and environmental studies. This study provides a comprehensive and sequential approach for the reconstruction of groundwater level data near Lake Uluabat in Bursa, Turkey. This study addresses missing data reconstruction for both past and future events using the Gradient Boosting Regression (GBR) model. The reconstruction process is evaluated through model calibration metrics and changes in the statistical properties of the observed and reconstructed time series. To achieve this goal, the groundwater time series from two observational wells and lake water levels during the January 2004 to September 2019 period are used. The lake water level, the definition of the four seasons via the application of three dummy variables, and time are used as inputs in the prediction of groundwater levels in observation wells. The optimal GBR model calibration is achieved by training the dataset selected based on data gaps in the time series, while test-past and test-future datasets are used for model validation. Afterward, the GBR models are used in reconstructing the missing data both in the pre- and post-training data sets, and the performance of the models are evaluated via the Nash-Sutcliffe efficiency (NSE), Root Mean Square Percentage Error (RMSPE) and Performance Index (PI). The statistical properties of the time series including the probability distribution, maxima, minima, quartiles (Q1-Q3), standard error (SE), coefficient of variation (CV), entropy (H), and error propagation are also measured. It was concluded that GBR provides a good base for missing data reconstruction (the best performance was as high as NSE: 0.99, RMSPE: 0.36, and PI: 1.002). In particular, the standard error and the entropy of the system in one case, respectively, experienced a 53% and 35% rise, which was found to be tolerable and negligible.Öğe Drought Assessment in the Aegean Region of Turkey(Springer Basel Ag, 2022) Mersin, Denizhan; Gulmez, Ayse; Safari, Mir Jafar Sadegh; Vaheddoost, Babak; Tayfur, GokmenDrought indices are commonly used to monitor the duration and severity of droughts. In this regard, the continuously changing climate regardless of its cause or effect pushes the limit of the water deficit through time and space. Izmir is a raising city in Turkey, which owns various water resources including but not limited to seashores, lakes, river streams, and groundwater aquifers. In this study, the long-term precipitation and temperature records from 14 meteorological stations between 1973 and 2020 (for 47 years) are used to investigate the drought characteristics in Buyuk Menderes, Kucuk Menderes, and Gediz basins located in the Aegean region of Turkey. For this, the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), Percent of Normal (PNI), and the so-called Discrepancy Precipitation Index (DPI) are used with consideration to 1-, 3-, 6-, and 12-month moving averages to investigate the drought patterns. Results showed that the monthly indices depict very similar results for the entire region. However, in the 1980s and 2010s droughts were more severe than the rest of the historical records. When the moving average operator is implemented in the analysis (3-, 6- and 12-month periods), neither SPI nor the SPEI showed the same results at any stations. It is illustrated that the periods of severe and normal drought have occurred in the past, yet the indices that are obtained using average values are generally within the normal limits, but extreme values (extremely arid or extremely wet) occurred occasionally. It is also concluded that although there is a similarity between the implemented indices, the DPI and PNI depict the highest resemblance.Öğe 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, BabakThe 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.Öğe Estimation of flow duration and mass flow curves in ungauged tributary streams(Elsevier Sci Ltd, 2023) Vaheddoost, Babak; Yilmaz, Mustafa Utku; Safari, Mir Jafar SadeghThe mastery in forecasting the streamflow rates is of great importance in the design, planning and resilience against droughts. Likewise, the application of flow duration and mass flow curves in the design of the reservoir capacity, energy generation, water allocation, etc. especially at the tributary reaches is a great challenge mostly due to the lack of information and data records. In this study, we have developed a methodology to obtain the flow duration curve (FDC) and mass flow curve (MFC) in tributary stream stations with the help of estimated streamflow rates. The procedure suggests using two alternative approaches in the selection of the reference station on the mainstream. The streamflow in the reference station is decomposed into direct runoff (DR) and base flow (BF) using one-parameter digital filter method. Together with the precipitation records in the tributary station, the DR and BF on the reference station are then used to estimate the FDC and MFC. The multivariate adaptive regression spline (MARS) and random forest (RF) methods are used to alternate each other, and the residual of the models are simulated using the autoregressive conditionally heteroscedastic (ARCH) approach to develop the hybrid MARS-ARCH and RF-ARCH models. A data set related to Coruh River Basin, in Turkey is used to confirm the methodology, while results with R2 >= 0.92, reasonable bias, and relative error in the estimation of the expected FDC and MFC rates indicated the robustness of the suggested methodology.Öğe Evaluating the impact of subsurface hydraulic barriers on Qanat flow rates using quantile regression forest(Nature Portfolio, 2025) Can, Murat; Vaheddoost, Babak; Safari, Mir Jafar SadeghQanats, as hydraulic innovations, enabled the sustainable extraction and distribution of groundwater for irrigation and domestic use during history. This study presents a data-driven modeling framework that implements Quantile Regression Forest (QRF), Random Forest (RF), and Support Vector Regression (SVR) to predict Qanat discharge under altered subsurface conditions. Using field data from the Dirsak Qanat in northern Iran, a traditional drainage system recently enhanced by the construction of a subsurface dam (SD), we investigate the dam's effect on discharge potential. The modeling framework incorporates determination of multiple hydro-meteorological inputs including precipitation, temperature, evaporation, humidity, runoff depth, infiltration depth and groundwater levels observed at three monitoring wells. A binary (dummy) variable was also introduced to represent the presence or absence of the SD, thereby capturing the associated changes in boundary conditions. The analysis further revealed that the SD and evaporation are the most influential factors, highlighting the combined effects of anthropogenic modifications and climatic variations on the discharge behavior of the Qanats. It was also concluded that the QRF model with a Nash-Sutcliffe Efficiency (NSE) of 0.818, demonstrate strong predictive capability in capturing complex and non-linear hydrological interactions.Öğe Evaluation of Streamflow Drought Index in Aegean Region, Turkey(Springer International Publishing Ag, 2022) Gulmez, Ayse; Mersin, Denizhan; Vaheddoost, Babak; Safari, Mir Jafar SadeghWater is an invaluable substance of which ensures the life cycle and hydrological events across the world. In this respect, water deficit also known as drought is a natural disaster related to water scarcity in time and space. Although there is no solid definition for the phenomenon, the outcome of repeated wet and dry spells cause in economic, social, and political problems at regional, country-wide, and world-wide scale. In this study, drought associated with the streamflow in the Aegean region, which has an important economic, historical and wsocio-cultural role in the western Turkey, is investigated through the well-known streamflow drought index (SDI). Therefore, average discharge in the Cicekli-Nif, Besdegirmenler-Dandalas, Bebekler-Rahmanlar and Kocarli-Koprubasi station respectively related to on Gediz, Buyuk Menderes and Kucuk Menderes basins were used. Then SDI with 1, 3, 6,12 months moving average are acquired to express the drought severity associated with the streamflow in the basins. Results showed that the SDI values in all of stations together with the 1, 3, 6, and 12-month moving averages depicts similar results and no abnormal situation exist during the study period.Öğe Evaluation of the Static and Pseudo-Static Stability and Effectiveness of an Improvement Technique for Slopes of the Vanyar Dam Reservoir(Korean Society Of Civil Engineers-Ksce, 2021) Ahbab, Amirhossein; Akhlaghi, Tohid; Safari, Mir Jafar Sadegh; Avcı, EyübhanExcavation on the inclined surfaces on the dam reservoir and rising water level may also affect the slope stability of the inclined surfaces in the dam reservoir under static and dynamic conditions. In this study, it is aimed to present a three-dimensional (3D) model to analyze slope stability of access road to the dam's crest and calculates the value of FOS in process of instructing and exploitation of dam and estimating the possibility of landslide occurrence during excavations and impounding of the dam. To this end, analysis of the slope stability has been implemented based on the information obtained from the field inspections, investigations, geological surveys, manual and mechanical borings in laboratory and field experiments. For acquiring the value of factor of safety (FOS), an explicit-finite-difference code is implemented. Effects of excavations in different levels of slope and fluctuation of water table in instability of the slope have been analyzed. The outcomes reveal that through increasing the level of water, FOS is decreased and large amounts of soil were entered in the dam's reservoir, blocking the entrance of the drainage valve and disrupt access way to the dam crest. Therefore, piles in the different distance have been used for controlling the slope stability and the best distribution of piles based on acceptable values for factor of safety in different regulations have been determined. It was observed that the excavation on the slope and increment of the water level in the dam reservoir influence the slope stability.Öğe Fast multi-output relevance vector regression for joint groundwater and lake water depth modeling(Elsevier Sci Ltd, 2022) Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Vaheddoost, BabakFast multi-output relevance vector regression (FMRVR) algorithm is developed for simultaneous estimation of groundwater and lake water depth for the first time in this study. The FMRVR is a multi-output regression analysis technique which can simultaneously predict multiple outputs for a multi-dimensional input. The data used in this study is collected from 34 stations located in the lake Urmia basin over a 40-year time period. The performance of the FMRVR model is examined in contrast to the support vector regression (SVR) and multi-linear regression (MLR) benchmarks. Results reveal that FMRVR is able to generate more accurate estimation for groundwater and lake water depth with coefficient of determination (R2) of 0.856 and 0.992 and root mean square error (RMSE) of 0.857 and 0.083, respectively. The outperformance of FMRVR can be linked to its capability for a joint estimation of multiple relevant outputs by taking into account possible correlations among the outputs.Öğe Flow and turbulence characteristics of bed load sediment transport for self-cleansing without deposition(Wiley, 2025) Kohandel Gargari, Mehrnoush; Keskin, Ilayda; Safari, Mir Jafar Sadegh; Vaheddoost, BabakInvestigating the structure of flow turbulence and bed load sediment transport is crucial as it provides insights into the functioning of aquatic environments, where such variations can lead to changes in ecosystem dynamics. This study focuses on the impact of sediments on the hydraulic characteristics of flow at self-cleansing without deposition conditions of sediment transport. The self-cleansing without deposition is not only a mode of sediment transport in alluvial channels, but it also serves as a criterion for the design of lined channels. Among the various design concepts for lined open channels, such as sewers and drainage channels, self-cleansing without deposition condition is implemented as the most conservative and reliable approach. However, most of the conducted experimental studies on self-cleansing without deposition have focused on measuring the basic flow and sediment characteristics for modelling purposes and neglected the effect of bed load, sediment size, flow discharge, and channel bed slope on turbulence characteristics. This study addresses this gap by examining the impact of bed load, sediment size, bed slope, and discharge on turbulence characteristics through a series of experiments conducted in a 12.5 m flume with a rectangular cross-section, equipped with an automatic control system (ACS) at the Hydraulic Laboratory of Ya & scedil;ar University. The channel bed slope, sediment discharge, flow discharge, and depth were adjusted and measured using ACS. Discharge and flow depth were measured using an ultrasonic flow-meter and depth sensors, respectively. Flow characteristics were measured using a Vectrino profiler device. The study reveals that bed load sediment transport reduces streamwise velocity, especially for coarse particles. Additionally, at a constant bed slope, velocity differences remain small at lower discharges but become more significant as discharge increases. Turbulence intensity rises with bed load motion, more in the streamwise direction than vertically. At a constant bed slope, increasing discharge enhances turbulence, but the effect is more pronounced at lower slopes and less significant at steeper slopes. Reynolds shear stress increases with particle size and steeper slopes, indicating greater shear production. These observations suggest critical implications for the design and optimization of open-channel systems, emphasizing the need for detailed consideration of particle sizes and bed conditions in engineering practices.Öğe Historical Trends Associated with Annual Temperature and Precipitation in Aegean Turkey, Where Are We Heading?(Mdpi, 2022) Mersin, Denizhan; Tayfur, Gokmen; Vaheddoost, Babak; Safari, Mir Jafar SadeghThe trend analysis of annual temperature (daily average) and total precipitation has been conducted for 14 stations located in the Aegean Region, Turkey. The Sen, Spearman's rho, and Mann-Kendall test methods are used in the detection of the historical trends in the region. The Pettitt test is also implemented to find the significance of the trend, while the Theil-Sen approach is applied to detect the change point(s) in the time series. Findings of the following study indicate that both precipitation and temperature time series in the selected stations depict statistically significant trends with increasing nature. The rate of increase in precipitation and temperature by the Theil-Sen test is found to be 4.2-7.9 mm/year and 0.20-0.35 degrees C/decade, respectively. It is also found that the turn points of the temperature trends determined by the Pettitt test occurred in 1998 for all the stations. According to the results, the magnitude of the extreme events would change in the future, which may help in conceptualizing the framework and the resilience of the infrastructures against climate change.Öğe Meteorological Drought Assessment in Mountainous Regions Based on Outputs of General Circulation Models(CRC Press, 2023) Balov, Mustafa Nuri; Vaheddoost, Babak; Safari, Mir Jafar SadeghThe availability of water among the different parts of the hydrological cycle has a significant impact on the environment and ecological balance as well as on the food industry and tourism. Drought, which can be considered as the shortage of available water in time and/or space, has become more frequent and intense in recent years. From the ecological point of view, studying the effect of climatic variables on the quantity and quality of flora and fauna needs multidisciplinary comprehensive research and measurements. However, the various impacts of extreme climatic events such as droughts on the economy, health, and welfare of society are highly interconnected with the environmental and ecological considerations of the climate system. © 2024 Taylor & Francis Group, LLC.Öğe Multiple kernel fusion: A novel approach for lake water depth modeling(Academic Press Inc Elsevier Science, 2023) Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Vaheddoost, BabakMultiple kernel fusion (MKF) refers to the task of combining multiple sources of information in the Hilbert space for improved performance. Very often the combined kernel is formed as a linear composition of multiple base kernels where the combination weights are learned from the data. As the first application of an MKF approach in hydrological modeling, lake water depth as one of the pivot factors in the reservoir analysis is simulated by considering different hydro-meteorological variables. The role of each individual input parameter is initially investigated by applying a kernel regression approach. We then illustrate the utility of an MKF formalism which learns kernel combination of weights to yield an optimal composition for kernel regression. A set of 40-year data collected from 27 groundwater and streamflow stations and 7 meteorological stations for precipitation and evaporation parameters in the vicinity of Lake Urmia are utilized for model development. Both visual and quantitative statistical performance criteria illustrate a superior performance for the MKF approach compared to kernel ridge regression (KRR), the support vector regression (SVR), back propagation neural network (BPNN) and auto regressive (AR) models. More specifically, while each individual input parameter fails to provide an accurate prediction for lake water depth modeling, an optimal combination of all input parameters incorporating the groundwater level, streamflow, precipitation and evaporation via a multiple kernel learning approach enhances the predictive performance of the model accuracy in the multiple scenarios. The promising results (RMSE = 0.098 m; R2 = 0.987; NSE = 0.986) may motivate the application of a MKF approach towards solving alternative and complex hydrological problems.Öğe Non-Linear Output Structure Learning: A Novel Multi-Target Technique for Multi-Station and Multi-Index Drought Modelling(Wiley, 2025) Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Vaheddoost, BabakExiting artificial intelligence-based drought models estimate a single drought index in a single station. This study advances drought modelling by proposing Non-linear Output Structure Learning (NOSL) for simultaneously estimating two drought indices at eight stations. A multi-target drought model provides insights for a better understanding of the meteorological and hydrological impacts of drought. Hydro-meteorological data, including precipitation, evaporation, and streamflow, are used for a joint estimation of Streamflow Drought Index (SDI) and Standardized Precipitation Evapotranspiration Index (SPEI). The efficacy of the NOSL algorithm is examined against single-target Kernel Ridge Regression (KRR) and Fast Multi-output Relevance Vector Regression (FMRVR) models. The data during October 1981 to September 2015 at a monthly scale (408 Months) from eight different stations in Buyuk Menderes Basin (BMB) located (BMB) in Western T & uuml;rkiye are used in this study. The effects of 1-, 3-, and 6-month Moving Average (MA) are also considered for drought estimation. Results show that NOSL can effectively estimate the SPEI and SDI indices and outperforms KRR and FMRVR benchmarks. The effectiveness of the NOSL technique can be linked to a structural modelling mechanism based on vector-valued functions, where the dependencies among output variables are captured utilising a non-linear function for enhanced performance. The developed multi-target drought model based on the NOSL technique not only helps in incorporating multiple variables in the model for a better estimation, but it enhances our understanding of various aspects of droughts and building adaptive strategies and resilience map counter to drought hazard.Öğe Projected Drought Intensification in the Büyük Menderes Basin Under CMIP6 Climate Scenarios(Mdpi, 2025) Rotbeei, Farzad; Nuri Balov, Mustafa; Safari, Mir Jafar Sadegh; Vaheddoost, BabakThe amplitude and interval of drought events are expected to enhance in upcoming years resulting from global warming and climate alterations. Understanding future drought events' potential impacts is important for effective regional adaptation and mitigation approaches. The main goal of this research is to study the impacts of climate change on drought in the B & uuml;y & uuml;k Menderes Basin located in the Aegean region of western T & uuml;rkiye by using the outcomes of three general circulation models (GCMs) from CMIP6 considering two different emission scenarios (SSP2-4.5 and SSP5-8.5). Following a bias correction using a linear scaling method, daily precipitation and temperature projections are used to compute the Standardized Precipitation Evapotranspiration Index (SPEI). The effectiveness of the GCMs in projecting precipitation and temperature is evaluated using observational data from the reference period (1985-2014). Future drought conditions are then assessed based on drought indices for three periods: 2015-2040 (near future), 2041-2070 (mid-term future), and 2071-2100 (late future). Consequently, the number of dry months is projected and expected to elevate, informed by SSP2-4.5 and SSP5-8.5 scenarios, during the late-century timeframe (2071-2100) in comparison to the baseline period (1985-2014). The findings of this study offer an important understanding for crafting adaptation strategies aimed at reducing future drought impacts in the B & uuml;y & uuml;k Menderes Basin in the face of changing climate conditions.
