Modelling of gully erosion risk using new ensemble of conditional probability and index of entropy in Jainti River basin of Chotanagpur Plateau Fringe Area, India

The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and maintaining the correspondence between different spheres of the environment. The major goal of this study is to evaluate the gully erosion susceptibility in the rugged topography of the Hinglo River Basin of eastern India, which ultimately contributes to sustainable land management practices. Due to the nature of data instability, the weakness of the classifier andthe ability to handle data, the accuracy of a single method is not very high. Thus, in this study, a novel resampling algorithm was considered to increase the robustness of the classifier and its accuracy. Gully erosion susceptibility maps have been prepared using boosted regression trees (BRT), multivariate adaptive regression spline (MARS) and spatial logistic regression (SLR) with proposed resampling techniques. The re-sampling algorithm was able to increase the efficiency of all predicted models by improving the nature of the classifier. Each variable in the gully inventory map was randomly allocated with 5-fold cross validation, 10-fold cross validation, bootstrap and optimism bootstrap, while each consisted of 30% of the database. The ensemble model was tested using 70% and validated with the other 30% using the K-fold cross validation (CV) method to evaluate the influence of the random selection of training and validation database. Here, all resampling methods are associated with higher accuracy, but SLR bootstrap optimism is more optimal than any other methods according to its robust nature. The AUC values of BRT optimism bootstrap, MARS optimism bootstrap and SLR optimism bootstrap are 87.40%, 90.40% and 90.60%, respectively. According to the SLR optimism bootstrap, the 107,771 km2 (27.51%) area of this region is associated with a very high to high susceptible to gully erosion. This potential developmental area of the gully was found primarily in the Hinglo River Basin, where lateral exposure was mainly observed with scarce vegetation. The outcome of this work can help policy-makers to implement remedial measures to minimize the damage caused by erosion of the gully.

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Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

Remote Sensing ◽

10.3390/rs12213620 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3620

Author(s):

Indrajit Chowdhuri ◽

Subodh Chandra Pal ◽

Alireza Arabameri ◽

Asish Saha ◽

Rabin Chakrabortty ◽

...

Keyword(s):

Land Use Changes ◽

Area Under The Curve ◽

Regression Tree ◽

Gully Erosion ◽

Support Vector ◽

Boosted Regression Tree ◽

Conditioning Factors ◽

Susceptibility Maps ◽

Chotanagpur Plateau ◽

Additive Regression

The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.

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Comparative Analysis between Morphometry and Geo-Environmental Factor Based Soil Erosion Risk Assessment Using Weight of Evidence Model: a Study on Jainti River Basin, Eastern India

Environmental Processes ◽

10.1007/s40710-019-00388-5 ◽

2019 ◽

Vol 6 (4) ◽

pp. 883-913 ◽

Cited By ~ 7

Author(s):

Tusar Kanti Hembram ◽

Gopal Chandra Paul ◽

Sunil Saha

Keyword(s):

Risk Assessment ◽

Comparative Analysis ◽

Soil Erosion ◽

Environmental Factor ◽

River Basin ◽

Weight Of Evidence ◽

Eastern India ◽

Erosion Risk ◽

Soil Erosion Risk ◽

Evidence Model

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Assessing gully erosion susceptibility in Mayurakshi river basin of eastern India

Environment Development and Sustainability ◽

10.1007/s10668-018-0224-x ◽

2018 ◽

Vol 22 (2) ◽

pp. 883-914 ◽

Cited By ~ 9

Author(s):

Sandipta Debanshi ◽

Swades Pal

Keyword(s):

River Basin ◽

Gully Erosion ◽

Eastern India

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Responses of chemical weathering and gully erosion causing land degradation in the extended part of Chotanagpur plateau in India

10.5194/egusphere-egu2020-6607 ◽

2020 ◽

Author(s):

Subodh Chandra Pal ◽

Rabin Chakrabortty

Keyword(s):

Decision Tree ◽

Land Degradation ◽

Chemical Weathering ◽

Large Scale ◽

Spectroscopic Analysis ◽

Controlling Factors ◽

Gully Erosion ◽

Plateau Region ◽

Essential Information ◽

Chotanagpur Plateau

Whether the hot and humid subtropical plateau region could leads to land degradation in the form of weathering and gully erosion. In this study, chemical weathering, gully erosion and cohesiveness are investigated together to bring out a new comprehensive idea with a view to understand their controlling factors. This study aimed to address potential land degradation in the extended part of Chotanagpur plateau region. The layers of controlling factors of gully erosion were developed and prioritized considering advanced decision tree, decision tree and random forest algorithms in the R software and the results of these methods were also validated using receiver operating characteristic (ROC) curves. Degree of chemical weathering and cohesiveness were measured through the chemical, physical and spectroscopic analysis of the randomly collected 412 soil samples. Apart from this, the climatic elements like temperature and rainfall were considered for estimating the chemical weathering. The results of the gully erosion models have superb accuracy, i.e. ROC values were 0.970, 0.960 and 0.955 respectively. Therefore, advanced decision tree model has been integrated with the results of degree of chemical weathering and cohesiveness in GIS platform end eventually the land degradation map has been developed. The land degradation map shown that 15% of the study area is highly affected by land degradation whereas 18% area is moderately affected by land degradation and rest of the 67% area is less affected by land degradation. This study provides essential information to the policy makers in order to taking decision for minimizing and controlling the land degradation. This innovative comprehensive approach is significant to assess degradation of existing land to a large scale.Keywords: Land degradation; weathering; cohesiveness; gully erosion; spectroscopic analysis

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