Browsing by Author "Khosravi, Khabat"

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Citation - WoS: 15
Citation - Scopus: 17
Clear-water scour depth prediction in long channel contractions: Application of new hybrid machine learning algorithms
(Elsevier Ltd, 2021) Khabat Khosravi; Mir Jafar Sadegh Safari; James R. Cooper; Khosravi, Khabat; Safari, Mir Jafar Sadegh; Cooper, James R.
Scour depth prediction and its prevention is one of the most important issues in channel and waterway design. However the potential for advanced machine learning (ML) algorithms to provide models of scour depth has yet to be explored. This study provides the first quantification of the predictive power of a range of standalone and hybrid machine learning models. Using previously collected scour depth data from laboratory flume experiments the performance of five types of recently developed standalone machine learning techniques - the Isotonic Regression (ISOR) Sequential Minimal Optimization (SMO) Iterative Classifier Optimizer (ICO) Locally Weighted learning (LWL) and Least Median of Squares Regression (LMS) - are assessed along with their hybrid versions with Dagging (DA) and Random Subspace (RS) algorithms. The main findings are five-fold. First the DA-ICO model had the highest prediction power. Second the hybrid models had a higher prediction power than standalone models. Third all algorithms underestimated the maximum scour depth except DA-ICO which predicted scour depth almost perfectly. Fourth scour depth was most sensitive to densimetric particle Froude number followed by the non-dimensionalized contraction width flow depth within the contraction sediment geometric standard deviation approach flow velocity and median grain size. Fifth most of the algorithms performed best when all the input parameters were involved in the building of the model. An important exception was the best performing model that required only four input parameters: densimetric particle Froude number non-dimensionalized contraction width flow depth within the contraction and sediment geometric standard deviation. Overall the results revealed that hybrid machine learning algorithms provide more accurate predictions of scour depth than empirical equations and traditional ML-algorithms. In particular the DA-ICO model not only created the most accurate predictions but also used the fewest easily and readily measured input parameters. Thus this type of model could be of real benefit to practicing engineers required to estimate maximum scour depth when designing in-channel structures. © 2021 Elsevier B.V. All rights reserved.
Citation - Scopus: 59
Daily river flow simulation using ensemble disjoint aggregating M5-Prime model
(Elsevier Ltd, 2024) Khabat Khosravi; Nasrin Fathollahzadeh Attar; Sayed M. Bateni; Changhyun Jun; Dongkyun Kim; Mir Jafar Sadegh Safari; Salim Heddam; Aitazaz Ahsan Farooque; Soroush Abolfathi; Safari, Mir Jafar Sadegh; Abolfathi, Soroush; Jun, Changhyun; Bateni, Sayed M.; Kim, Dongkyun; Attar, Nasrin; Khosravi, Khabat
Accurate prediction of daily river flow (Qt) remains a challenging yet essential task in hydrological modeling particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt as well as one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The predictive performance of M5P ensembles incorporating Bootstrap Aggregation (BA) Disjoint Aggregating (DA) Additive Regression (AR) Vote (V) Iterative classifier optimizer (ICO) Random Subspace (RS) and Rotation Forest (ROF) were comprehensively evaluated. The proposed models were applied to a case study data in Tuolumne County US using a dataset comprising measured precipitation (Pt) evaporation (Et) and Qt. A wide range of input scenarios were explored for predicting Qt Qt+1 and Qt+2. Results indicate that Pt and Qt significantly influence prediction accuracy. Notably relying solely on the most correlated variable (e.g. Qt-1) does not guarantee robust prediction of Qt. However extending the forecast horizon mitigates the influence of low-correlation input variables on model accuracy. Performance metrics indicate that the DA-M5P model achieves superior results with Nash-Sutcliff Efficiency of 0.916 and root mean square error of 23 m3/s followed by ROF-M5P BA-M5P AR-M5P AR-M5P RS-M5P V-M5P ICO-M5P and the standalone M5P model. The ensemble M5P modeling framework enhanced the predictive capability of the stand-alone M5P algorithm by 1.2 %–22.6 % underscoring its efficacy and potential for advancing hydrological forecasting. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 37
Citation - Scopus: 42
Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction
(SPRINGER HEIDELBERG, 2021) Sarita Gajbhiye Meshram; Mir Jafar Sadegh Safari; Khabat Khosravi; Chandrashekhar Meshram; Safari, Mir Jafar Sadegh; Meshram, Sarita Gajbhiye; Meshram, Chandrashekhar; Khosravi, Khabat
Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin Chhattisgarh State India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period (1980-2015). The accuracy of the developed models is examined in terms of error, by root mean square error (RMSE) and mean absolute error (MAE), and based on a correlation index of determination coefficient (R-2). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers.
Citation - WoS: 4
Citation - Scopus: 5
Stacking ensemble-based hybrid algorithms for discharge computation in sharp-crested labyrinth weirs
(Springer Science and Business Media Deutschland GmbH, 2022) Khabat Khosravi; Mir Jafar Sadegh Safari; Zohreh Sheikh Khozani; Brian Mark Crookston; Ali Golkarian; Golkarian, Ali; Sheikh Khozani, Zohreh; Safari, Mir Jafar Sadegh; Khozani, Zohreh Sheikh; Crookston, Brian; Khosravi, Khabat
Labyrinth weirs are utilized to transport a greater discharge during floods in contrast to conventional weirs due to their increased weir crest length. Nevertheless due to the increased geometric complexity of labyrinth weirs determination of accurate discharge coefficients and accordingly head-discharge ratings are quite essential issues in practical application. Hence as a first step the present study proposes the following eight standalone algorithms: decision table (DT) Kstar least median square (LMS) M5 prime (M5P) M5 rule (M5R) pace regression (PR) random forest (RF) and sequential minimal optimization (SMO). Then applying the stacking (ST) algorithm these standalone models were hybridized to predict the discharge coefficient (Cd) for sharp-crested labyrinth weirs. Potential/effective variables were constructed in the form of several independent dimensionless parameters (i.e. θ h/W L/B L/h Froude number (Fr) B/W and L/W) to predict Cd as an output. The accuracy of the developed models was examined in terms of different statistical visually based and quantitative-based error measurement criteria. The results illustrate that h/W and B/W parameters have the highest and lowest effect on the Cd prediction respectively. According to NSE all developed algorithms provided accurate performances while ST-Kstar had the highest prediction power. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Suspended Sediment Modeling Using Sequential Minimal Optimization Regression and Isotonic Regression Algorithms Integrated with an Iterative Classifier Optimizer
(Birkhauser, 2022) Mir Jafar Sadegh Safari; Sarita Gajbhiye Meshram; Khabat Khosravi; Adel Moatamed; Safari, Mir Jafar Sadegh; Meshram, Sarita Gajbhiye; Khosravi, Khabat; Moatamed, Adel
Suspended sediment load modeling through advanced computational algorithms is of major importance and a challenging topic for developing highly accurate hydrological models. To model the suspended sediment load in the Rampur watershed station in the Mahanadi River Basin Chhattisgarh State India unique integrated computational intelligence regression models with an optimizer are proposed in this study. For the first time in the literature the isotonic regression (ISO) and sequential minimal optimization regression (SMOR) models and their hybrid versions with an iterative classifier optimizer (ICO) are applied for suspended sediment load modeling. The research is based on daily discharge and suspended sediment data collected over a 38-year period (1976–2014). Root mean square error (RMSE) relative root mean square error (RRMSE) coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) were employed to evaluate the performance of the standalone ISO and SMOR as well as the proposed ICO–ISO and ICO–SMOR hybrid models. Ten different scenarios were considered for modeling to investigate the performance of the models using different input combinations. The proposed new models were found to be more reliable than standalone ISO and SMOR models. Results revealed that the performance of the hybrid model was mostly attributable to the basic algorithm for the model development where both SMOR and ICO–SMOR models were superior to their ISO and ICO–ISO counterparts in terms of accurate computation. Overall the ICO–SMOR models outperformed the other models in terms of accuracy with RMSE RRMSE R2 and NSE of 5495.1 tons/day 2.77 0.90 and 0.86 respectively. The current study's findings support the applicability of the proposed methodology for modeling of suspended sediment load and encourage the use of these methods in alternative hydrological modeling. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 20
Citation - Scopus: 22
Weighted instances handler wrapper and rotation forest-based hybrid algorithms for sediment transport modeling
(ELSEVIER, 2021) Katayoun Kargar; Mir Jafar Sadegh Safari; Khabat Khosravi; Kargar, Katayoun; Khosravi, Khabat; Safari, Mir Jafar Sadegh
Sediment transport modeling has been known as an essential issue and challenging task in water resources and environmental engineering. In order to minimize the adverse impacts of the continues sediment deposition that is known as a main source of pollution in the urban area the self-cleansing method is widely utilized for designing the sewer pipes to create a condition to keep the bottom of channel clean from sedimentation. In the present study an extensive data range is utilized for modeling the sediment transport in non-deposition with clean bed condition. Regarding the effective parameters involved four different scenarios are considered for the modeling. To this end four standalone methods including the M5P reduced error pruning tree (REPT) random forest (RF) and random tree (RT) and two hybrid models based on rotation forest (ROF) and weighted instances handler wrapper (WIHW) techniques are developed and result compared with three empirical equations. Based on the results the hybrid WIHW-RT and WIHW-RF models provide better performance in particle Froude number estimation in comparison to other standalone and hybrid models. Performances of the most of the models are found accurate except RT and REPT standalone models. The outcomes revealed that the empirical models have considerable overestimation. Generally hybrid data mining methods yield more precise estimations of sediment transport in contrast to the regression equations and standalone models. Particularly both WIHW-RT and WIHWRF models provide almost the same performances however as WIHW-RT can better capture the extreme particle Froude number values it slightly outperforms WIHW-RF. Promising findings of the current study may encourage the implementation of the recommended approaches in alternative hydrological problems.