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Corrigendum: Hydrology Research 50 (6), 1645–1664: Difference in the bed load transport of graded and uniform sediments during floods: An experimental investigation, Khabat Khosravi, Amir H. N. Chegini, Andrew D. Binns, Prasad Daggupati and Luca Mao, https://doi.org/10.2166/nh.2019.078

Hydrology Research, 2021-12, Vol.52 (6), p.1648-1648

Khosravi, Khabat

2021

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A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran

The Science of the total environment, 2018-06, Vol.627, p.744-755

2018

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Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models

Environmental monitoring and assessment, 2016-12, Vol.188 (12), p.656-656, Article 656

2016

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A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

Journal of hydrology (Amsterdam), 2019-06, Vol.573, p.311-323

2019

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A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction

Journal of hydrology (Amsterdam), 2021-05, Vol.596, p.126100, Article 126100

2021

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Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan

The Science of the total environment, 2019-04, Vol.662, p.332-346

2019

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Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

Di xue qian yuan., 2021-03, Vol.12 (2), p.505-519

2021

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Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran

Journal of hydrology (Amsterdam), 2020-12, Vol.591, p.125552, Article 125552

2020

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

Environmental science and pollution research international, 2021-03, Vol.28 (9), p.11637-11649

2021

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Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Sensors (Basel, Switzerland), 2018-11, Vol.18 (11), p.3777

2018

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Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Sensors (Basel, Switzerland), 2018-07, Vol.18 (8), p.2464

2018

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Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water (Basel), 2019-07, Vol.11 (7), p.1402

2019

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A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models

Environmental science and pollution research international, 2021-09, Vol.28 (36), p.50525-50541

2021

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Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

Remote sensing (Basel, Switzerland), 2019-07, Vol.11 (13), p.1589

2019

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

Water (Basel), 2018-09, Vol.10 (9), p.1210

2018

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Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping

Hydrogeology journal, 2019-11, Vol.27 (7), p.2511-2534

2019

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Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

Applied sciences, 2019-09, Vol.9 (18), p.3755

2019

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Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China

Natural hazards (Dordrecht), 2019-06, Vol.97 (2), p.579-609

2019

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Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches

Acta geophysica, 2023-04, Vol.71 (2), p.1015-1031

2023

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Spatial variability of soil water erosion: Comparing empirical and intelligent techniques

Di xue qian yuan., 2023-01, Vol.14 (1), p.1-16, Article 101456

2023

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

Scientific reports, 2021-07, Vol.11 (1), p.15152-15152, Article 15152

2021

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Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images

International journal of environmental research and public health, 2020-06, Vol.17 (12), p.4210

2020

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A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping

Water (Basel), 2019-10, Vol.11 (10), p.2013

2019

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Multi-step ahead soil temperature forecasting at different depths based on meteorological data: Integrating resampling algorithms and machine learning models

Pedosphere, 2023-06, Vol.33 (3), p.479-495

2023

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Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?

Engineering applications of computational fluid mechanics, 2024-12, Vol.18 (1)

2024

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Corrigendum: Hydrology Research 50 (6), 1645–1664: Difference in the bed load transport of graded and uniform sediments during floods: An experimental investigation, Khabat Khosravi, Amir H. N. Chegini, Andrew D. Binns, Prasad Daggupati and Luca Mao, https://doi.org/10.2166/nh.2019.078

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran

Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models

A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction

Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran

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

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

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

Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China

Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches

Spatial variability of soil water erosion: Comparing empirical and intelligent techniques

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

Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images

A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping

Multi-step ahead soil temperature forecasting at different depths based on meteorological data: Integrating resampling algorithms and machine learning models

Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?

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