Machine learning models for wetland habitat vulnerability in mature Ganges delta

Pal, Swades; Debanshi, Sandipta

doi:10.1007/s11356-020-11413-8

Cited by 17 publications

(2 citation statements)

References 137 publications

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“…At present, scholars in this eld have different de nitions of vulnerability, so the regional ecological vulnerability assessment framework is mainly divided into two categories (Berrouet et al 2019). ( 1 In the study of ecological vulnerability evaluation, the combination of RS and GIS technology and knowledge-driven method or data mining method and the vulnerability evaluation using spatial and nonspatial data have been widely used (Reichenbach et al 2018 Pal et al 2021). These vulnerability studies show that machine learning models provide better results in vulnerability evaluations than traditional methods because nonlinear data can be adequately processed by machine learning models with various scales .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the ecological vulnerability of Chongqing using deep learning

Liu

et al. 2023

Environ Sci Pollut Res

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The ecological environment is the welfare of human survival. With the global climate change in recent years, the deterioration of the ecological environment has exceeded any time. The purpose of this study is to evaluate the ecological vulnerability of Chongqing, China, and draw an ecological vulnerability map.The study screened the impact factors by information gain ratio, and nally considered 16 ecological vulnerability impact factors, using multilayer perception (DNN) and convolutional neural network (CNN) methods to model vulnerability. A total of 1200 ecological points were recorded by remote sensing images, eld survey and local data, and divided into training set and test set according to the ratio of 7: 3. Finally, two ecological vulnerability status maps were produced. The DNN and CNN models were evaluated by receiver operating characteristic curve (ROC), area under curve (AUC), mean absolute error (MAE) and root mean square error (RMSE). The results showed that the ecological vulnerability evaluation effect of CNN (AUC = 0.926) was better than that of DNN (AUC = 0.888). Calculate the contribution rate of vulnerability impact factors to different model results by random forests. The results show that the highest contribution rate of the two models are geological factors. It can be understood that the relative ecological vulnerability of Chongqing is mainly affected by its own karst landform. The areas with high vulnerability in the study area are the northeast and southeast regions, as well as the mountain valleys and urban in the central and western regions. The main ecological problems are low forest quality and unreasonable structure caused by its own geological factors, and serious rocky deserti cation and soil erosion. Human activities, including deforestation, over-reclamation and stone mining, are also important factors contributing to the ecological vulnerability of the study area. The machine learning method of this study creates an appropriate and accurate ecological vulnerability status map, which can support the future ecological environment protection and governance decisions in the study area.

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Section: Introductionmentioning

confidence: 99%

Evaluating the ecological vulnerability of Chongqing using deep learning

Liu

et al. 2023

Environ Sci Pollut Res

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“…Machine Learning (ML) algorithms based spatial models coupled with and Geographical Information System (GIS) has been used to address different issues of the wide array of disciplines including epidemiology (Auchincloss et al, 2012), climate change (Mansfield et al, 2020), natural resource (Frey, 2020), environmental vulnerability (Pal and Debanshi, 2021), environmental hazards (Wang et al, 2021) etc. Gully erosion is one of the major issues that have become a global concern with the increasing coverage of degraded land across the world (UNCCD, 2013).…”

Section: Introductionmentioning

confidence: 99%

Point and pixel inclusive machine learning models for exploring gully erosion susceptibility

Pal

Debanshi

Paul³

2022

Geocarto International

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Sample point based spatial model derived from Machine Learning (ML) algorithms often generalizes the spatial pattern of event. The present study has tried to highlight how far it is acceptable and can it replace pixel based modelling? The present study has presented a comparative view of pixel and sample point based modelling of gully erosion susceptibility of the upper Mayurakshi basin to assess the predictabilities. Random forest (RF), Support Vector Machine (SVM) and (ADB) were applied for developing pixel and point based models in WEKA and Python software environments respectively. From the models, it is found that 14-25% area located mainly in the upper parts of the study unit is very highly susceptible to gully erosion. Based on the accuracy and performance level using Area under curve (AUC) of Receiver operating curve, sensitivity, precision, F1 score, MCC pixel based ensemble models are superior to point based modelling. Often, the point based models have a very poor agreement with training and testing data. So, pixel based models could not be replaced with point based models. RF is found as the best representative model. The study, therefore, recommends using pixel based modelling for this or a similar purpose. Since the models have figured out the gully erosion susceptible areas, it would be a useful tool for related planning processes.

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Hybrid Tree-Based Wetland Vulnerability Modelling

Pal

Paul

2022

Springer Natural Hazards

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Machine learning models for wetland habitat vulnerability in mature Ganges delta

Cited by 17 publications

References 137 publications

Evaluating the ecological vulnerability of Chongqing using deep learning

Evaluating the ecological vulnerability of Chongqing using deep learning

Point and pixel inclusive machine learning models for exploring gully erosion susceptibility

Hybrid Tree-Based Wetland Vulnerability Modelling

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