Exploration
Devraj Dhakal; Kanwarpreet Singh
Abstract
Landslides pose significant risks to human life, infrastructure, and the environment, particularly in geologically unstable regions like the Himalayas. This study aims to develop and validate landslide susceptibility maps using Frequency Ratio (FR) and Information Value (IV) models within a GIS framework. ...
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Landslides pose significant risks to human life, infrastructure, and the environment, particularly in geologically unstable regions like the Himalayas. This study aims to develop and validate landslide susceptibility maps using Frequency Ratio (FR) and Information Value (IV) models within a GIS framework. Employing high-resolution geospatial data, including geomorphological, topographical, and hydrological factors derived from high-resolution digital elevation models (DEMs) and other geospatial datasets. The susceptibility maps were classified into five categories: Low, Moderate, High, Very High, and Extremely High. The models were trained and validated using a landslide inventory of 1313 landslide events, with a 70:30 split for training and testing datasets. The predictive performance of the models was evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve, yielding AUC values of 84.1 for the FR model and 83.9 for the IV model. The Landslide Density Index (LDI) further confirmed the models' reliability, indicating higher landslide densities in the predicted high-susceptibility zones. The study demonstrates that both FR and IV models are effective tools for landslide susceptibility mapping and its validation. The findings highlight the FR model's superior predictive accuracy in this specific area. Future research should leverage advanced machine learning techniques, such as XGBoost, Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (KNN), to enhance the reliability and precision of landslide susceptibility models.
Devraj Dhakal; Salad Omar Abdi; Kanwarpreet Singh; Abhishek Sharma
Abstract
The highway contributes significantly to human existence by providing safe, dependable, and cost-effective services that are environmentally friendly and promote economic progress. Highway projects need extensive planning to prevent work revisions, save time and cost, and increase job efficiency. Without ...
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The highway contributes significantly to human existence by providing safe, dependable, and cost-effective services that are environmentally friendly and promote economic progress. Highway projects need extensive planning to prevent work revisions, save time and cost, and increase job efficiency. Without a doubt, Highway transportation system must be constantly updated to keep up with technology breakthroughs, environmental change, and rising client needs. Incorporating Remote Sensing (RS) and Geographic Information Systems (GISs) has the potential to go beyond the limitations of RS, which typically collects information about the earth and its peripheries from space, and does not alter, analyze, calculate, query or display geographic engineering maps. Over the last few decades, the fusion of RS and GIS has shown promise, and the researchers are employing it in different stages of the Highway Planning and Development Process (HPDP) such as optimal route analysis, geometric design, operation and management, traffic modeling, accident analysis, and environmental impact analysis (noise pollutions, air pollutions). This paper gives an overall review of the use of RS and GIS on HPDP at various stages of their lifecycles.
