Abstract
Rising populations in urban centers, across developing nations, have promoted rapid urbanization and urban growth. While the construction industry has enjoyed continuous development, it generates a large amount of construction and demolition waste (CDW). With an annual production of 150 million tonnes, CDW management has grown to be a significant environmental problem in the majority of Indian towns. A conservative 1.3% of CDW is recycled, 10–30% is utilized for backfilling, and the rest is illegally openly dumped. This study seeks to address the problem of CDW management by developing a municipality-level CDW collection route network. Predictive probable illegal dumpsite mapping has been carried out by analyzing the spatial distribution of already-existing sites and investigating factors influencing their occurrence. The primary data utilized include a list of 310 illegal dumpsites identified using photointerpretation of Google Earth imagery and UAV data for the years 2019-2022 and 27 potentially significant socioeconomic and physiographic characteristics. Current dumpsite mapping and prediction analysis were conducted using spatial statistics and GIS techniques. Through spatial modeling, the predicted locations of illegal dumpsites are mapped in relation to risk level. Additionally, a network of intermediate designated collecting stations around the municipality has been created based on high-risk regions employing mapping techniques for different decision support system-based planning. This has been linked to land use information discovered through the examination of satellite images. With the relevant thematic and spatial data, this technique will help to define future occurrences and may also be applied to different spatial environments.
Presenters
Shubhi NunaStudent, M.Tech, Indian Institute of Technology Kharagpur, West Bengal, India Bharath H Aithal
Brajesh Dubey
Indian Institute of Technology Kharagpur
Details
Presentation Type
Theme
KEYWORDS
Predictive Modeling, Spatial Analysis, Waste Management, Decision Support System
