Khabat Khosravi

Florida International University

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Binh Thai Pham

University Of Transport Technology

Mahdi Panahi

Stockholm University

Dieu Tien Bui

University of South-Eastern Norway

Himan Shahabi

University of Kurdistan

Kamran Chapi

University of Kurdistan

Ali Golkarian

Ferdowsi University of Mashhad

Ataollah Shirzadi

University of Kurdistan

Assefa M. Melesse

Florida International University

James R. Cooper

University of Liverpool

Saro Lee

Korea Institute of Geoscience and Mineral Resources

Cooperative Institutions

University of Tehran

University of Tabriz

Sari Agricultural Sciences and Natural Resources University

Duy Tan University

University of Kurdistan

University of Technology Malaysia

Ton Duc Thang University

Agricultural Research & Education Organization

Islamic Azad University, Science and Research Branch

Ferdowsi University of Mashhad

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Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Scientific Reports (2018)

Dieu Tien Bui Mahdi Panahi Himan Shahabi Vijay P. Singh Ataollah Shirzadi

Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.

Topographic Wetness Index

Ensemble forecasting

10.1038/s41598-018-33755-7

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Citations (155)

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

Environmental Science and Pollution Research (2020)

Sarita Gajbhiye Meshram Mir Jafar Sadegh Safari Khabat Khosravi Chandrashekhar Meshram

10.1007/s11356-020-11335-5

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Citations (41)

Author Correction: Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Scientific Reports (2021)

Dieu Tien Bui Mahdi Panahi Himan Shahabi Vijay P. Singh Ataollah Shirzadi

10.1038/s41598-021-93957-4

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Citations (2)

Improving groundwater potential mapping using metaheuristic approaches

Hydrological Sciences Journal (2020)

Seyed Vahid Razavi-Termeh Khabat Khosravi Abolghasem Sadeghi‐Niaraki Soo-Mi Choi Vijay P. Singh

Due to climate change and urban growth, the demand for new freshwater sources, especially groundwater, is increasing in water-deficient countries like Iran. Therefore, this study aimed at groundwater potential mapping (GPM) of the Nahavand Plain, Iran, using an optimized adaptive neuro fuzzy inference system (ANFIS) in a geographic information system, with three metaheuristic optimization algorithms: differential evolution (DE), particle swarm optimization (PSO) and ant colony optimization (ACO). A spatial database was constructed using 273 spring locations and 14 groundwater conditioning factors. The optimization algorithms were evaluated using the receiver operating characteristic (ROC) technique. The ANFIS-DE, ANFIS-PSO and ANFIS-ACO models resulted in accuracy of 0.816, 0.809 and 0.758, respectively; the high and very high potential for groundwater springs covered 26% of the Nahavand Plain. The Root Mean Square Error (RMSE) for the training and validation datasets was lowest for the ANFIS-DE model compared to the other two models; and the ANFIS-PSO model had a higher convergence speed. These results may play an important role in sustainable groundwater management in the Nahavand Plain.

Differential Evolution

10.1080/02626667.2020.1828589

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Citations (38)

Improving prediction of water quality indices using novel hybrid machine-learning algorithms

The Science of The Total Environment (2020)

Duie Tien Bui Khabat Khosravi John P. Tiefenbacher Hoang Nguyen Nerantzis Kazakis

Pruning

Tree (set theory)

10.1016/j.scitotenv.2020.137612

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Citations (304)

AI-driven predictions of geophysical river flows with vegetation

Scientific Reports (2024)

Sanjit Kumar Mayank Agarwal Vishal Deshpande James R. Cooper Khabat Khosravi

In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques-Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R

KSTAR

Pruning

10.1038/s41598-024-67269-2

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Citations (1)

Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm

The Science of The Total Environment (2020)

Dieu Tien Bui Khabat Khosravi Mahshid Karimi Gianluigi Busico Zohreh Sheikh Khozani

10.1016/j.scitotenv.2020.136836

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Citations (71)

New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling

Water (2018)

Dieu Tien Bui Khabat Khosravi Shaojun Li Himan Shahabi Mahdi Panahi

This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains.

Topographic Wetness Index

Stream power

10.3390/w10091210

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Citations (213)

Assessing groundwater drought vulnerability through baseflow separation and index-based analysis under climate change projections

Groundwater for Sustainable Development (2024)

Mostafa Riazi Khabat Khosravi Majid Riahi Samani Shasha Han Saeid Eslamian

Base flow

SWAT model

10.1016/j.gsd.2024.101179

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