Threats of soil erosion under CMIP6 SSPs scenarios: an integrated data mining techniques and geospatial approaches

Saha, Asish; Pal, Subodh Chandra; Chowdhuri, Indrajit; Islam, Abu Reza Md. Towfiqul; Chakrabortty, Rabin; Roy, Paramita

doi:10.1080/10106049.2022.2127925

Cited by 8 publications

(3 citation statements)

References 141 publications

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“…In addition, this model has other advantages such as: (i) it is less prone to overfitting as it builds trees sequentially, and each tree focuses on correcting the errors of the previous ones, (ii) it can effectively handle features with different scales, and (iii) it can more effectively handle outliers of the data used. The findings of this work are also in line with the other published works (Nguyen et al 2021a;Saha et al 2022).…”

Section: Validation Of the Modelssupporting

confidence: 93%

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Quyet Nguyen,

Thi Tran,

Thanh Thi Nguyen

et al. 2023

Journal of Hydroinformatics

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Soil Erosion Susceptibility Mapping (SESM) is one of the practical approaches for managing and mitigating soil erosion. This study applied four Machine Learning (ML) models namely the Multilayer Perceptron (MLP) classifier, AdaBoost, Ridge classifier, and Gradient Boosting classifier to perform SESM in a region of Nghe An province, Vietnam. The development of these models incorporated seven factors influencing soil erosion: slope degree, slope aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), rainfall, and soil type. These factors were determined based on 685 identified soil erosion locations. According to SHapley Additive exPlanations (SHAP) analysis, soil type emerged as the most significant factor influencing soil erosion. Among all the developed models, the Gradient Boosting classifier demonstrated the highest prediction power, followed by the MLP classifier, Ridge classifier, and AdaBoost, respectively. Therefore, the Gradient Boosting classifier is recommended for accurate SESM in other regions too, taking into account the local geo-environmental factors.

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Section: Validation Of the Modelssupporting

confidence: 93%

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Quyet Nguyen,

Thi Tran,

Thanh Thi Nguyen

et al. 2023

Journal of Hydroinformatics

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“…Most of the previous studies endorse the use of RFR and SVR in the ensemble of precipitation and temperature (Ahmed et al, 2019, 2020; Dey et al, 2022; Jose et al, 2022; Li et al, 2021; Yang et al, 2022). Asadollah et al (2022) has proved the efficacy of the Gradient Boosting Regression Tree (GBRT) in the downscaling of GCMs over RFR and SVR, XGBR in the land degradation study (Saha et al, 2022), and drought‐maize yield dynamics study (Muthuvel et al, 2023) using CMIP6 GCMs. Over the river basins of the WG, the XGBR MME has a higher ability to capture peak temperature and precipitation than other MME models.…”

Section: Resultsmentioning

confidence: 99%

“…The applicability of ML-based methods in various parts of the world continues to be debated, despite the development of more sophisticated ones. The studies have proved the efficacy of Gradient Boosting Regression Trees in downscaling of GCMs (Asadollah et al, 2022), land degradation identification (Saha et al, 2022) and drought crop yield dynamics (Muthuvel et al, 2023). But limited studies have been done on the performance of these AdaBoost and XGboost ensemble methods in the MME of GCMs.…”

Section: Introductionmentioning

confidence: 99%

The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India

Shetty

Umesh

Shetty

2023

Intl Journal of Climatology

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The popularity of cutting‐edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top‐performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi‐Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo‐climatic basins. Precipitation is best simulated by EC‐Earth3 and BCC‐CSM2‐MR. Maximum temperature by MPI‐ESM1‐2‐HR, EC‐Earth3‐Veg, INM‐CM5‐0 and MPI‐ESM1‐2‐LR. Minimum temperature by INM‐CM5‐0 and MPI‐ESM1‐2‐LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per‐humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms.

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Land use and land cover classification using machine learning algorithms in google earth engine

Ahmed,

2023

Earth Sci Inform

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Threats of soil erosion under CMIP6 SSPs scenarios: an integrated data mining techniques and geospatial approaches

Cited by 8 publications

References 141 publications

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India

Land use and land cover classification using machine learning algorithms in google earth engine

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