Thesis Abstract

Abstract
Landslides pose significant threats to human lives, infrastructure, and the environment, making their accurate prediction and mapping crucial for effective risk assessment and mitigation strategies. This research investigates landslide susceptibility mapping using remote sensing and Geographic Information System (GIS) techniques in a selected region. The study area, located in [Specify Region], has experienced recurrent landslides, leading to property damage and loss of lives. The primary objective of this research is to develop a comprehensive landslide susceptibility map that can aid in proactive planning and disaster management. The research methodology involves a multi-step approach, including data collection, preprocessing, feature selection, model development, and validation. Remote sensing data, such as satellite imagery and Digital Elevation Models (DEMs), will be utilized to extract relevant terrain parameters, including slope, aspect, elevation, and land cover. GIS software will be employed to integrate these spatial datasets and apply statistical and machine learning algorithms to establish relationships between landslide occurrences and contributing factors. Chapter 2 of the thesis provides a detailed literature review, covering existing studies on landslide susceptibility mapping, remote sensing applications, GIS techniques, and machine learning algorithms. The review synthesizes key findings and methodologies to inform the research design and approach. Chapter 3 outlines the research methodology, including data collection strategies, preprocessing techniques, feature selection methods, and model development procedures. The chapter elaborates on the selection criteria for the study area, data sources, and the rationale behind the chosen remote sensing and GIS tools. Chapter 4 presents the discussion of findings, where the developed landslide susceptibility map is evaluated and compared with existing hazard maps. The results of the study are analyzed in terms of prediction accuracy, model performance, and practical implications for land use planning and disaster risk reduction. Chapter 5 concludes the thesis by summarizing the key findings, highlighting the significance of the research outcomes, and providing recommendations for future research directions. The study contributes to the field of geoscience by demonstrating the effectiveness of remote sensing and GIS techniques in landslide susceptibility mapping and emphasizing the importance of proactive risk management strategies in landslide-prone regions. Overall, this research aims to enhance our understanding of landslide dynamics and provide valuable insights for policymakers, urban planners, and stakeholders to mitigate the risks associated with landslides in the selected region and similar areas worldwide.

Thesis Overview