Yazar "Mohammadi, Babak" seçeneğine göre listele

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    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 Utku
    Accurate 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.
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    A spatiotemporal teleconnection study between Peruvian precipitation and oceanic oscillations
    (Pergamon-Elsevier Science Ltd, 2020) Mohammadi, Babak; Vaheddoost, Babak; Mehr, Ali Danandeh
    Large-scale oceanic oscillations and their teleconnections with meteorological events are of great importance in macro-scale climatic studies. In this regard, this study investigates the spatiotemporal teleconnections between four oceanic oscillations, namely North Atlantic Oscillation (NAO), El Nino/Southern Oscillation (ENSO), Atlantic Multi-Decadal Oscillation (AMO), and Pacific Decadal Oscillation (PDO), against Peruvian precipitation patterns during the past 25 years (i.e., 1990-2015). For this purpose, variation in the precipitation pattern at monthly and annual scales as well as the Standardized Precipitation Index (SPI) time series at 1-, 3-, 12-, and 48-month time scales were evaluated at 10 meteorology stations across Peru. Pearson's correlation coefficient and mutual information between the oceanic oscillations and precipitation-born signals were calculated and spatially interpolated using the Kriging method. The results indicated the presence of three major climatic regions in the country. The NAO has the largest correlation with the monthly precipitation. However, the ENSO was found as the main climate driver of extremely wet and extremely dry conditions in the country. The results also demonstrated that the PDO has a higher impact on the annual precipitation pattern, particularly in the southern and eastern parts of the country.
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    A study of the relationship between GRACE-TWSA and large-scale atmospheric-oceanic patterns
    (Taylor & Francis Ltd, 2025) Vaheddoost, Babak; Mohammadi, Babak
    This study aims to investigate the connections between changes in the Total Water Storage Anomaly (TWSA) associated with 12 continental/subcontinental regions derived from the Gravity Recovery and Climate Experiment (GRACE) satellite and 25 different Large-Scale Climate Oscillation Indices (LSCOI). Initially, correlation analyses were performed and then the principal component analyses together with wavelet coherent transform were employed in the analyses. The results explain over 50% of TWSA variability, with the Pacific and Indian Oceans exerting strong influence. While Southern Hemisphere regions display consistent long-term relationships with LSCOI patterns, the Northern Hemisphere exhibits more complex dynamics, including trade-offs between leading and lagging effects and in-phase versus anti-phase states. Random Forest and M5 Tree models were then applied using high-correlation LSCOIs as predictors for regional TWSAs. Results of models confirmed the robustness of the identified teleconnections and demonstrated the potential for predicting regional water storage anomalies using climate oscillation indices.
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    Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils
    (Springer Heidelberg, 2020) Vaheddoost, Babak; Guan, Yiqing; Mohammadi, Babak
    Field capacity (FC) and permanent wilting point (PWP) are two important properties of the soil when the soil moisture is concerned. Since the determination of these parameters is expensive and time-consuming, this study aims to develop and evaluate a new hybrid of artificial neural network model coupled with a whale optimization algorithm (ANN-WOA) as a meta-heuristic optimization tool in defining the FC and the PWP at the basin scale. The simulated results were also compared with other core optimization models of ANN and multilinear regression (MLR). For this aim, a set of 217 soil samples were taken from different regions located across the West and East Azerbaijan provinces in Iran, partially covering four important basins of Lake Urmia, Caspian Sea, Persian Gulf-Oman Sea, and Central-Basin of Iran. Taken samples included portion of clay, sand, and silt together with organic matter, which were used as independent variables to define the FC and the PWP. A 80-20 portion of the randomly selected independent and dependent variable sets were used in calibration and validation of the predefined models. The most accurate predictions for the FC and PWP at the selected stations were obtained by the hybrid ANN-WOA models, and evaluation criteria at the validation phases were obtained as 2.87%, 0.92, and 2.11% respectively for RMSE, R-2, and RRMSE for the FC, and 1.78%, 0.92, and 10.02% respectively for RMSE, R-2, and RRMSE for the PWP. It is concluded that the organic matter is the most important variable in prediction of FC and PWP, while the proposed ANN-WOA model is an efficient approach in defining the FC and the PWP at the basin scale.
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    ENN-SA: A novel neuro-annealing model for multi-station drought prediction
    (Pergamon-Elsevier Science Ltd, 2020) Mehr, Ali Danandeh; Vaheddoost, Babak; Mohammadi, Babak
    This paper presents a new hybrid model, called ENN-SA, for spatiotemporal drought prediction. In ENN-SA, an Elman neural network (ENN) is conjugated with simulated annealing (SA) optimization and support vector machine (SVM) classification algorithms for the standardized precipitation index (SPI) modeling at multiple stations. The proposed model could be applied to predict SPI at different time scales in a meteorology station with lack of data through the intelligent use of SPI series of the nearby stations as the model inputs. The capability of the hybrid model for multi-station prediction of meteorological drought was examined through the cross-validation technique for Kecioren station in Ankara Province, Turkey. To this end, the SPI-3, SPI-6, and SPI-12 at the station were modeled using the same indices of five nearby stations. In the first step, SVM was trained using different kernels in order to generate and classify a set of plausible multi-station prediction scenarios. Then, ENN was used to regress the SPI series at each scenario and finally, the SA component of the integrated model was utilized to improve the ENN efficiency. Various error and complexity measures were used to detect the models' performance. The results showed the ENN-SA is promising and efficient for multi-station SPI prediction.
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    The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey
    (Mdpi, 2023) Vaheddoost, Babak; Mohammadi, Babak; Safari, Mir Jafar Sadegh
    This study addressed the intricate interplay between meteorological droughts and groundwater level fluctuations in the vicinity of Mount Uludag in Bursa, Turkey. To achieve this, an exhaustive analysis encompassing monthly precipitation records and groundwater level data sourced from three meteorological stations and eight groundwater observation points spanning the period from 2007 to 2018 was performed. Subsequently, this study employed the Standard Precipitation Index (SPI) and Standard Groundwater Level (SGL) metrics, meticulously calculating the temporal extents of drought events for each respective time series. Following this, a judicious application of both the Thiessen and Support Vector Machine (SVM) methodologies was undertaken to ascertain the optimal groundwater observation wells and their corresponding SGL durations, aligning them with SPI durations tied to the selected meteorological stations. The SVM technique, in particular, excelled in the identification of the most pertinent observation wells. Additionally, the Elman Neural Network (ENN) and its optimized version through the Firefly Algorithm (ENN-FA), demonstrated their prowess in accurately predicting SPI durations based on SGL durations. The results were favorable, as evidenced by the commendable performance metrics of the Normalized Root Mean Square Error (NRMSE), the Nash-Sutcliffe Efficiency (NSE), the product of the coefficient of determination and the slope of the regression line (bR2), and the Kling-Gupta Efficiency (KGE). Consequently, the favorable simulation results were construed as evidence supporting the presence of a discernible association between SGL and the duration of the SPI. As we substantiate the concordance between the temporal extent of meteorological droughts and the perturbations in groundwater levels, this unmistakably underscores the fact that the historical fluctuations in groundwater levels within the region were predominantly attributable to climatic influences, rather than being instigated by anthropogenic activities. Nevertheless, it is imperative to underscore that this revelation should not be misconstrued as an endorsement of future heedless exploitation of groundwater resources.