Product Overview (Chapter) | COPAL Publishing Group

Dr Manjari Chakraborty | Dr Rizwan Kazmi

ISBN:

978-81-180058-1-0

DOI:

https://doi.org/10.67509/ceci5910

8. Circular Economy for Disaster Debris in Hilly Tourist Destinations: A Machine Learning Approach

Circular Economy in the Construction Industry: Waste Management Practices

Soura Manna | Ankhi Banerjee

DOI:

https://doi.org/10.67509/ceci5918

2026 | 337 pp.

The Himalayan region is one of the most geologically active areas in the world, rendering it highly vulnerable to natural disasters. The state of Sikkim, an integral part of the Eastern Himalayan Region, is immensely susceptible to these hazards due to heavy rainfall, a fragile ecosystem, and high seismic activity. Concurrently, the scenic Himalayan landscape attracts a significant number of domestic and foreign tourists, accounting for approximately one out of every ten jobs in the state. As a landlocked region with rugged terrain, Sikkim suffers from limited accessibility. Consequently, when a catastrophe strikes, connectivity across most of the state is severely disrupted, inducing socio- economic vulnerability within tourism-dependent communities.

In regions with challenging terrain, disaster response is extremely difficult and frequently delayed. Therefore, to accelerate the recovery process, building community capacity to leverage economic opportunities from disaster debris is a crucial step. To successfully implement the 3R (Reduce, Reuse, and Recycle) model of a circular economy, it is essential to ensure the prompt identification of the extent of regional damage, mapping of debris spread, recovery of reusable construction materials, and determination of optimized transport routes.

This study focuses on the rapid identification of damaged sites and the detection of building materials within from disaster debris composition. Various machine learning models—including Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN)—were utilized, leveraging satellite imagery and field photographs from the 2023 flash flood site in North Sikkim. Disaster debris flow extent identification using MLC, SVM, and RF yielded an accuracy of over 99%, with RF proving to be the most suitable choice for this task, while CNN provided highly reliable outcomes for material feature extraction. In post-disaster debris management, combining community involvement and the 3R model with machine learning tools can significantly improve the efficiency of restoring connectivity. Ultimately, capacity building for rapid disaster response employing a circular economy framework is an essential step toward building resilient communities.

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