Knowledge-Based Landslide Susceptibility Zonation System-Reference-Cited by-同舟云学术

Knowledge-Based Landslide Susceptibility Zonation System

Published:2010-07 Issue:4 Volume:24 Page:325-334
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Ghosh Jayanta Kumar¹²,Bhattacharya Devanjan¹²

Affiliation:

1. Assistant Professor, Dept. of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India (corresponding author).

2. Research Scholar, Dept. of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000034

Reference43 articles.

1. Landslide hazard assessment: summary review and new perspectives

2. Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain

3. Evaluation of Knowledge Representation Schemes as a Prerequisite toward Development of a Knowledge-Based System

4. A fast string searching algorithm

5. Bureau of Indian Standard (BIS). (1998). “Indian standard preparation of landslide hazard zonation maps in mountainous terrains—Guidelines. Part 2: Macro-zonation.” IS14496 New Delhi 1–19.

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing landslide susceptibility mapping using a positive-unlabeled machine learning approach: a case study in Chamoli, India;Geoenvironmental Disasters;2024-07-18

2. Remote Sensing and GIS-Based Accuracy Assessment of LULC Map and Landslide Susceptibility Prediction for Meghalaya, India;Journal Of The Geological Society Of India;2024-05-01

3. Landslide susceptibility mapping for West-Jaintia Hills district, Meghalaya;Sādhanā;2024-02-01

4. Landslide Susceptibility Assessment Based on Machine Learning Techniques;Disaster Risk Reduction;2024

5. Identification of the significant parameters in spatial prediction of landslide hazard;Bulletin of Engineering Geology and the Environment;2023-07-13