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Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source

Scientific reports, 2022-11, Vol.12 (1), p.19342-19342, Article 19342

2022

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Mapping wind erosion hazard with regression-based machine learning algorithms

Scientific reports, 2020-11, Vol.10 (1), p.20494-16, Article 20494

2020

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Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theory

Scientific reports, 2022-09, Vol.12 (1), p.15167-15167, Article 15167

2022

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A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust

Environmental science and pollution research international, 2020-11, Vol.27 (33), p.42022-42039

2020

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An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques

Scientific reports, 2024-08, Vol.14 (1), p.18951-16

2024

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Detection of land subsidence using hybrid and ensemble deep learning models

Applied geomatics, 2024-09, Vol.16 (3), p.593-610

2024

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Modeling land susceptibility to wind erosion hazards using LASSO regression and graph convolutional networks

Frontiers in environmental science, 2023-05, Vol.11

2023

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Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk

Journal of environmental management, 2023-11, Vol.345, p.118838-118838, Article 118838

2023

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Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion

The Science of the total environment, 2023-12, Vol.904, p.166960-166960, Article 166960

2023

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Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence

Environmental science and pollution research international, 2023-02, Vol.30 (10), p.26580-26595

2023

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Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory

Catena (Giessen), 2021-05, Vol.200, p.105178, Article 105178

2021

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Spatial modelling of soil salinity: deep or shallow learning models?

Environmental science and pollution research international, 2021-08, Vol.28 (29), p.39432-39450

2021

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Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind

Environmental pollution (1987), 2024-02, Vol.342, p.123082-123082, Article 123082

2024

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Mapping of the wind erodible fraction of soil by bidirectional gated recurrent unit (BiGRU) and bidirectional recurrent neural network (BiRNN) deep learning models

Catena (Giessen), 2023-04, Vol.223, p.106953, Article 106953

2023

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Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran

Aeolian research, 2021-03, Vol.50, p.100682, Article 100682

2021

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Integrated modelling for mapping spatial sources of dust in central Asia - An important dust source in the global atmospheric system

Atmospheric pollution research, 2021-09, Vol.12 (9), p.101173, Article 101173

2021

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Flood Prioritization of Dehbar Watersheds in Khorasan Razavi Province using TOPSIS Model, Morphometric Analysis and Regional Flood Frequency Analysis

journal of watershed management research, 2022-05, Vol.13 (25), p.188-196

2022

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Mapping of the Wind Erodible Fraction of Soil by Bidirectional Gated Recurrent Unit (Bigru) and Bidirectional Recurrent Neural Network (Birnn) Deep Learning Models

SSRN Electronic Journal

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Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source

Mapping wind erosion hazard with regression-based machine learning algorithms

Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theory

A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust

An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques

Detection of land subsidence using hybrid and ensemble deep learning models

Modeling land susceptibility to wind erosion hazards using LASSO regression and graph convolutional networks

Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk

Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion

Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence

Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory

Spatial modelling of soil salinity: deep or shallow learning models?

Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind

Mapping of the wind erodible fraction of soil by bidirectional gated recurrent unit (BiGRU) and bidirectional recurrent neural network (BiRNN) deep learning models

Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran

Integrated modelling for mapping spatial sources of dust in central Asia - An important dust source in the global atmospheric system

Flood Prioritization of Dehbar Watersheds in Khorasan Razavi Province using TOPSIS Model, Morphometric Analysis and Regional Flood Frequency Analysis

Mapping of the Wind Erodible Fraction of Soil by Bidirectional Gated Recurrent Unit (Bigru) and Bidirectional Recurrent Neural Network (Birnn) Deep Learning Models

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