Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning
This study presents landslide susceptibility (LS) prediction model using the Adaptive Neuro Fuzzy Inference System (ANFIS) and Geographic Information System (GIS) which incorporates the physiographic information. Such models are is useful for forest road planning. To this aim, a set of factors inclu...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
Research Institute of Forests and Rangelands of Iran
2014-11-01
|
| Series: | تحقیقات جنگل و صنوبر ایران |
| Subjects: | |
| Online Access: | http://ijfpr.areeo.ac.ir/article_12435_a259a3ae3fdb8f4748d44116279ee358.pdf |
| _version_ | 1819060432803987456 |
|---|---|
| author | Ismaeil Ghajar Akbar Najafi |
| author_facet | Ismaeil Ghajar Akbar Najafi |
| author_sort | Ismaeil Ghajar |
| collection | DOAJ |
| description | This study presents landslide susceptibility (LS) prediction model using the Adaptive Neuro Fuzzy Inference System (ANFIS) and Geographic Information System (GIS) which incorporates the physiographic information. Such models are is useful for forest road planning. To this aim, a set of factors including the terrain slope, aspect, geology formation, curvature, distance to rivers, and distance to faults at occurred landslide points were integrated into the ANFIS model. The modeling using a subtractive clustering method returned a coefficient of determination (R2) of 0.73 and a root mean square error (RMSE) of 0.27 for the best model. The sensitivity analysis indicated the distance to the rivers, geology formation, terrain slope, curvature, distance to the faults, and aspect as the most effective factors on the landslide occurrence. Furthermore, an evaluation of existing roads on simulated LS map showed that the majority of the currently existing roads are located on “medium” and “high” LS classes. |
| first_indexed | 2024-12-21T14:26:54Z |
| format | Article |
| id | doaj.art-2d073bfb05874e37a133b0529a170bbd |
| institution | Directory Open Access Journal |
| issn | 1735-0883 2383-1146 |
| language | fas |
| last_indexed | 2024-12-21T14:26:54Z |
| publishDate | 2014-11-01 |
| publisher | Research Institute of Forests and Rangelands of Iran |
| record_format | Article |
| series | تحقیقات جنگل و صنوبر ایران |
| spelling | doaj.art-2d073bfb05874e37a133b0529a170bbd2022-12-21T19:00:36ZfasResearch Institute of Forests and Rangelands of Iranتحقیقات جنگل و صنوبر ایران1735-08832383-11462014-11-0122350952610.22092/ijfpr.2014.1243512435Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planningIsmaeil Ghajar0Akbar Najafi1Associate Prof., Department of Forestry, Faculty of Natural Resources, Tarbiat Modares University, Noor, I.R. Iranدانشیار، دانشگاه تربیت مدرسThis study presents landslide susceptibility (LS) prediction model using the Adaptive Neuro Fuzzy Inference System (ANFIS) and Geographic Information System (GIS) which incorporates the physiographic information. Such models are is useful for forest road planning. To this aim, a set of factors including the terrain slope, aspect, geology formation, curvature, distance to rivers, and distance to faults at occurred landslide points were integrated into the ANFIS model. The modeling using a subtractive clustering method returned a coefficient of determination (R2) of 0.73 and a root mean square error (RMSE) of 0.27 for the best model. The sensitivity analysis indicated the distance to the rivers, geology formation, terrain slope, curvature, distance to the faults, and aspect as the most effective factors on the landslide occurrence. Furthermore, an evaluation of existing roads on simulated LS map showed that the majority of the currently existing roads are located on “medium” and “high” LS classes.http://ijfpr.areeo.ac.ir/article_12435_a259a3ae3fdb8f4748d44116279ee358.pdfLandslide susceptibilityNeuro-fuzzymodelANFISforest road |
| spellingShingle | Ismaeil Ghajar Akbar Najafi Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning تحقیقات جنگل و صنوبر ایران Landslide susceptibility Neuro-fuzzy model ANFIS forest road |
| title | Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning |
| title_full | Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning |
| title_fullStr | Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning |
| title_full_unstemmed | Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning |
| title_short | Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning |
| title_sort | modeling landslide susceptibility of a mountain forests using adaptive neuro fuzzy inference system anfis for forest road planning |
| topic | Landslide susceptibility Neuro-fuzzy model ANFIS forest road |
| url | http://ijfpr.areeo.ac.ir/article_12435_a259a3ae3fdb8f4748d44116279ee358.pdf |
| work_keys_str_mv | AT ismaeilghajar modelinglandslidesusceptibilityofamountainforestsusingadaptiveneurofuzzyinferencesystemanfisforforestroadplanning AT akbarnajafi modelinglandslidesusceptibilityofamountainforestsusingadaptiveneurofuzzyinferencesystemanfisforforestroadplanning |