Examination of Vegetation Health and Its Relation with Normalized Difference Built-Up Index

Goswami, Asutosh; Sen, Suhel; Chakraborty, P. K.

doi:10.1201/9781003221333-14

Advanced Sensing in Image Processing and IoT 2022

DOI: 10.1201/9781003221333-14

|View full text |Cite

Examination of Vegetation Health and Its Relation with Normalized Difference Built-Up Index

Asutosh Goswami¹,

Suhel Sen²,

P. K. Chakraborty³

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Preprint1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Comparative assessment of landslide susceptibility of West Sikkim using three machine learning techniques

Goswami¹,

Sen²,

Majumder³

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Natural disasters are responsible for disturbing the foundation of a stable system which affects man to a large extent. Man usually has no control on natural disasters. However, if precautionary measures are taken in advance, then the colossal loss of human lives and property caused by a disaster can be averted. In the world of rapidly developing technology, several machine learning algorithms along with GIS and Remote Sensing has been used by researchers to prepare disaster susceptibility map which in turn aided in adoption of appropriate mitigation measures. The present research work aimed for the preparation of landslide susceptibility map of West Sikkim district of Sikkim state in India by using three machine learning techniques namely Frequency Ratio (FR), Analytical Hierarchy Process (AHP) and Critic method. The study revealed that all these three techniques are ideal for landslide susceptibility mapping with AUC values of 0.824 for FR, 0.739 for AHP and 0.757 for Critic and hence concluded that FR is the best machine learning algorithm that can be used for preparing landslide susceptibility maps.

show abstract

Comparative assessment of landslide susceptibility of West Sikkim using three machine learning techniques

Goswami¹,

Sen²,

Majumder³

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Examination of Vegetation Health and Its Relation with Normalized Difference Built-Up Index

Cited by 1 publication

References 0 publications

Comparative assessment of landslide susceptibility of West Sikkim using three machine learning techniques

Comparative assessment of landslide susceptibility of West Sikkim using three machine learning techniques

Contact Info

Product

Resources

About