Comparative analysis of machine learning, statistical, and MCDA methods for rainfall-induced landslide susceptibility mapping in the ECO-sensitive Koyna river basin of India (Record no. 23546)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02117 a2200217 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251030104318.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251030b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Author | Patil, Abhijit S. |
| 9 (RLIN) | 27397 |
| 245 ## - TITLE STATEMENT | |
| Title | Comparative analysis of machine learning, statistical, and MCDA methods for rainfall-induced landslide susceptibility mapping in the ECO-sensitive Koyna river basin of India |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.55(2), Apr |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Mumbai |
| Name of publisher, distributor, etc. | Springer |
| Year | 2025 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 901-926p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This study aimed to identify the most effective method for landslide susceptibility mapping (LSM) in the Koyna River Basin, a region in the Western Ghats that is prone to devastating landslides. The study used a comparative analysis of three methods: support vector machine (SVM), frequency ratio (FR), and analytical hierarchy process (AHP) to determine the most effective method for LSM. A total of 1823 landslide events were identified and divided into training and test datasets. The study mapped various layers of landslide influencing factors and selected the optimal subset of factors using the information gain ratio. The susceptibility of landslides was mapped using SVM, FR, and AHP models, and the accuracies of all models were evaluated using the test dataset. The study showed that the SVM model was the most effective method with an area under the curve (AUC) value of 0.87, indicating acceptable prediction effectiveness. The LSM result of SVM was reliable and effective for implementation. The result was showing high landslide susceptibility in the forest area, particularly in the region of the Koyna Wildlife Sanctuary. The study provides valuable insights into effective methods for LSM in landslide-prone regions, which can help minimize losses and improve disaster management strategies. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Civil Engineering |
| 9 (RLIN) | 4621 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Co-Author | Teli, Shobha S. |
| 9 (RLIN) | 27398 |
| 773 0# - HOST ITEM ENTRY | |
| Title | Indian geotechnical journal |
| International Standard Serial Number | 0971-9555 |
| Place, publisher, and date of publication | Switzerland Springer |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://link.springer.com/article/10.1007/s40098-024-00957-y |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology (PG) | School of Engineering & Technology (PG) | Archieval Section | 30/10/2025 | 2025-1590 | 30/10/2025 | 30/10/2025 | Articles Abstract Database |