Browsing by Author "Mohammadi, Babak"

Now showing 1 - 7 of 7

Citation - WoS: 1
Citation - Scopus: 1
A multi-step strategy for enhancing the rainfall-runoff modeling: combination of lumped and artificial intelligence-based hydrological models
(SPRINGER, 2025) Babak Mohammadi; Mirali Mohammadi; Babak Vaheddoost; Mustafa Utku Yilmaz; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Yilmaz, Mustafa Utku; Mohammadi, Babak
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.
Citation - WoS: 78
Citation - Scopus: 83
IHACRES- GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling
(NATURE PORTFOLIO, 2022) Babak Mohammadi; Mir Jafar Sadegh Safari; Saeed Vazifehkhah; Vazifehkhah, Saeed; Safari, Mir Jafar Sadegh; Mohammadi, Babak
As a complex hydrological problem rainfall-runoff (RR) modeling is of importance in runoff studies water supply irrigation issues and environmental management. Among the variety of approaches for RR modeling conceptual approaches use physical concepts and are appropriate methods for representation of the physics of the problem while may fail in competition with their advanced alternatives. Contrarily machine learning approaches for RR modeling provide high computation ability however they are based on the data characteristics and the physics of the problem cannot be completely understood. For the sake of overcoming the aforementioned deficiencies this study coupled conceptual and machine learning approaches to establish a robust and more reliable RR model. To this end three hydrological process-based models namely: IHACRES GR4J and MISD are applied for runoff simulating in a snow-covered basin in Switzerland and then conceptual models' outcomes together with more hydro-meteorological variables were incorporated into the model structure to construct multilayer perceptron (MLP) and support vector machine (SVM) models. At the final stage of the modeling procedure the data fusion machine learning approach was implemented through using the outcomes of MLP and SVM models to develop two evolutionary models of fusion MLP and hybrid MLP-whale optimization algorithm (MLP-WOA). As a result of conceptual models the IHACRES-based model better simulated the RR process in comparison to the GR4J and MISD models. The effect of incorporating meteorological variables into the coupled hydrological process-based and machine learning models was also investigated where precipitation wind speed relative humidity temperature and snow depth were added separately to each hydrological model. It is found that incorporating meteorological variables into the hydrological models increased the accuracy of the models in runoff simulation. Three different learning phases were successfully applied in the current study for improving runoff peak simulation accuracy. This study proved that phase one (only hydrological model) has a big error while phase three (coupling hydrological model by machine learning model) gave a minimum error in runoff estimation in a snow-covered catchment. The IHACRES-based MLP-WOA model with RMSE of 8.49 m(3)/s improved the performance of the ordinary IHACRES model by a factor of almost 27%. It can be considered as a satisfactory achievement in this study for runoff estimation through applying coupled conceptual-ML hydrological models. Recommended methodology in this study for RR modeling may motivate its application in alternative hydrological problems.
Citation - WoS: 94
Citation - Scopus: 104
Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation
(Elsevier B.V., 2021) Babak Mohammadi; Yiqing Guan; Roozbeh Moazenzadeh; Mir Jafar Sadegh Safari; Guan, Yiqing; Moazenzadeh, Roozbeh; Safari, Mir Jafar Sadegh; Mohammadi, Babak
River suspended sediment load (SSL) estimation is of importance in water resources engineering and hydrological modeling. In this study a novel hybrid approach is recommended for SSL estimation in which multi-layer perceptron (MLP) is hybridized with particle swarm optimization (PSO) and then integrated with differential evolution algorithm (DE) called as MLP-PSODE. The hybrid MLP-PSODE model is implemented to model the SSL of Mahabad river located at northwest of Iran. For the sake of examination of the MLP-PSODE model performance several techniques including multi-layer perceptron (MLP) multi-layer perceptron integrated with particle swarm optimization (MLP-PSO) radial basis function (RBF) and support vector machine (SVM) are selected as benchmarks. For this purpose five different scenarios are considered for the modeling. The results indicated that the new hybrid model of MLP-PSODE is successful in estimating SSL by considering single input of discharge (Q) with high accuracy as compared to its alternatives with RMSE = 1794.4 ton·day−1 MAPE = 41.50% and RRMSE = 107.09% which were much lower than those of MLP based model with RMSE = 3133.7 ton·day−1 MAPE = 121.40% and RRMSE = 187.03%. The developed MLP-PSODE model not only outperforms its counterparts in terms of accuracy in extreme values estimation but also it is found as a parsimonious model that incorporates lower number of input parameters in its structure for SSL estimation. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 26
Citation - Scopus: 28
Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit
(Elsevier Ltd, 2020) Mir Jafar Sadegh Safari; Babak Mohammadi; Katayoun Kargar; Kargar, Katayoun; Safari, Mir Jafar Sadegh; Mohammadi, Babak
Inasmuch as channels are designed to mitigate continues sedimentation sediment transport models have been developed to calculate flow velocity to keep sediment particles in motion. In order to promote the computation capability of sediment transport models recently machine learning algorithms have attracted interests extensively. However accuracy of such a model is attributed to the range of data and applied technique for model construction. For this purpose the current study scrutinizes the applicability of “non-deposition with deposited bed” (NDB) concept for design of large channels applying hybrid machine learning algorithms. Through the modeling firstly conventional adaptive neuro-fuzzy inference system (ANFIS) technique is applied to develop a stand-alone model. In furtherance of improving the model's performance the ANFIS is hybridized with invasive weed optimization (IWO) algorithm to construct a hybrid ANFIS-IWO model. As a benchmark the ANFIS is further hybridized with classical genetic algorithm (GA) to compare with ANFIS-IWO outcomes. Furthermore the developed machine learning models are compared to multigene genetic programming (MGP) and particle swarm optimization (PSO) stand-alone machine learning results reported in the literature and classical regression models by means of variety of statistical performance measurements. Hybridization of ANFIS with IWO enhances its accuracy with a factor of 30%. Respecting to the models performance examination the ANFIS-IWO model is found superior to its alternatives for sediment transport computation. The thickness of the deposited bed and deposited bed width are found as effective parameters for sediment transport modeling in open channels with a bed deposit. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 29
Citation - Scopus: 37
Soil moisture estimation using novel bio-inspired soft computing approaches
(Taylor and Francis Ltd., 2022) Roozbeh Moazenzadeh; Babak Mohammadi; Mir Jafar Sadegh Safari; K. W. Chau; Moazenzadeh, Roozbeh; Chau, Kwok-wing; Safari, Mir Jafar Sadegh; Mohammadi, Babak
Soil moisture (SM) is of paramount importance in irrigation scheduling infiltration runoff and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA) krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature relative humidity wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68 MAPE = 0.04) and ANFIS (RMSE = 2.55 MAPE = 0.07) exhibited the best and worst performance in SM estimation respectively. All three hybrid models (ANFIS-WOA ANFIS-KHA and ANFIS-FA) improved SM estimates reducing RMSE by 34 28 and 27% relative to the base ANFIS model respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25) [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69 1.89 and 1.55 in the three SM intervals respectively. From the perspective of under- or over-estimation of moisture values ANFIS-WOA (RMSE = 1.44 MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94 MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall all three hybrid models performed better in the underestimation set compared to overestimation set. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 5
The Association between Meteorological Drought and the State of the Groundwater Level in Bursa Turkey
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Babak Vaheddoost; Babak Mohammadi; Mir Jafar Sadegh Safari; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Mohammadi, Babak
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. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 21
Citation - Scopus: 22
VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Ali Danandeh Mehr; Masoud Reihanifar; Mohammed Mustafa Alee; Mahammad Amin Vazifehkhah Ghaffari; Mir Jafar Sadegh Safari; Babak Mohammadi; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Ghaffari, Mahammad Amin Vazifehkhah; Vazifehkhah Ghaffari, Mahammad Amin; Reihanifar, Masoud; Mohammadi, Babak; Alee, Mohammad Mustafa; Danandeh Mehr, Ali
Meteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations the lack of observed data is the main leading cause of unfeasible sustainable watershed management plans. However various earth science and environmental databases are available that can be used for hydrological studies even at a catchment scale. In this study the Global Drought Monitoring (GDM) data repository that provides real-time monthly Standardized Precipitation and Evapotranspiration Index (SPEI) across the globe was used to develop a new explicit evolutionary model for SPEI prediction at ungauged catchments. The proposed model called VMD-GP uses an inverse distance weighting technique to transfer the GDM data to the desired area. Then the variational mode decomposition (VMD) in conjunction with state-of-the-art genetic programming is implemented to map the intrinsic mode functions of the GMD series to the subsequent SPEI values in the study area. The suggested model was applied for the month-ahead prediction of the SPEI series at Erbil Iraq. The results showed a significant improvement in the prediction accuracy over the classic GP and gene expression programming models developed as the benchmarks. © 2023 Elsevier B.V. All rights reserved.