Analysis of Landslide Vulnerability in Chefchaouen Using a Statistical Approach SIG

Context and background In the Rif region of northern Morocco, terrain instabilities pose significant social, economic, and environmental challenges. The city of Chefchaouen, located in the heart of the Rif mountain range, is particularly prone to landslides and slope instability due to its complex geology, geographic location, and periodic intense rainfall. These factors frequently trigger landslides, causing damage and disruption. Goal and Objectives The primary goal is to create a landslide susceptibility map to support effective land-use management and risk assessment. The objectives include: Identifying and analyzing key factors contributing to landslides. Utilizing landslide susceptibility mapping to guide risk management strategies.Minimizing the socio-economic impacts of terrain instabilities in Chefchaouen. Methodology A static method was used to assess nine conditioning factors for landslides: geology, slope, land use, altitude, aspect, proximity to roads and rivers, soil type, and precipitation. Data collection included a landslide inventory compiled by the urban municipality of Chefchaouen, high-resolution satellite imagery, and field verifications, identifying 400 instances of terrain instability. The dataset was split into two parts: 70% for training a predictive model and 30% for testing the model's performance using the AUC (Area Under Curve) metric. Slope exposure (orientation of a slope relative to geographic north) emerged as the factor with the highest prediction rate, at 8.711. Results The study produced a landslide susceptibility map that serves as a vital decision-making tool. This map highlights areas at risk and supports the development of risk management and land-use planning strategies, helping to mitigate the socio-economic consequences of terrain instability in Chefchaouen. Context and background In the Rif region of northern Morocco, terrain instabilities pose significant social, economic, and environmental challenges. The city of Chefchaouen, located in the heart of the Rif mountain range, is particularly prone to landslides and slope instability due to its complex geology, geographic location, and periodic intense rainfall. These factors frequently trigger landslides, causing damage and disruption. Goal and Objectives The primary goal is to create a landslide susceptibility map to support effective land-use management and risk assessment. The objectives include: Identifying and analyzing key factors contributing to landslides. Utilizing landslide susceptibility mapping to guide risk management strategies.Minimizing the socio-economic impacts of terrain instabilities in Chefchaouen. Methodology A static method was used to assess nine conditioning factors for landslides: geology, slope, land use, altitude, aspect, proximity to roads and rivers, soil type, and precipitation. Data collection included a landslide inventory compiled by the urban municipality of Chefchaouen, high-resolution satellite imagery, and field verifications, identifying 400 instances of terrain instability. The dataset was split into two parts: 70% for training a predictive model and 30% for testing the model's performance using the AUC (Area Under Curve) metric. Slope exposure (orientation of a slope relative to geographic north) emerged as the factor with the highest prediction rate, at 8.711. Results The study produced a landslide susceptibility map that serves as a vital decision-making tool. This map highlights areas at risk and supports the development of risk management and land-use planning strategies, helping to mitigate the socio-economic consequences of terrain instability in Chefchaouen. Context and background In the Rif region of northern Morocco, terrain instabilities pose significant social, economic, and environmental challenges. The city of Chefchaouen, located in the heart of the Rif mountain range, is particularly prone to landslides and slope instability due to its complex geology, geographic location, and periodic intense rainfall. These factors frequently trigger landslides, causing damage and disruption. Goal and Objectives The primary goal is to create a landslide susceptibility map to support effective land-use management and risk assessment. The objectives include: Identifying and analyzing key factors contributing to landslides. Utilizing landslide susceptibility mapping to guide risk management strategies.Minimizing the socio-economic impacts of terrain instabilities in Chefchaouen. Methodology A static method was used to assess nine conditioning factors for landslides: geology, slope, land use, altitude, aspect, proximity to roads and rivers, soil type, and precipitation. Data collection included a landslide inventory compiled by the urban municipality of Chefchaouen, high-resolution satellite imagery, and field verifications, identifying 400 instances of terrain instability. The dataset was split into two parts: 70% for training a predictive model and 30% for testing the model's performance using the AUC (Area Under Curve) metric. Slope exposure (orientation of a slope relative to geographic north) emerged as the factor with the highest prediction rate, at 8.711. Results The study produced a landslide susceptibility map that serves as a vital decision-making tool. This map highlights areas at risk and supports the development of risk management and land-use planning strategies, helping to mitigate the socio-economic consequences of terrain instability in Chefchaouen.

Institutions

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-11-28
DOI
https://doi.org/10.48346/imist.prsm/ajlp-gs.v7i6.53081
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Landslide Vulnerability in Chefchaouen Using a Statistical Approach SIG

SHILAP Revista de lepidopterología
Imbalanced Data Classification Techniques
article

Analysis of Landslide Vulnerability in Chefchaouen Using a Statistical Approach SIG

article en

Abstract

Context and background In the Rif region of northern Morocco, terrain instabilities pose significant social, economic, and environmental challenges. The city of Chefchaouen, located in the heart of the Rif mountain range, is particularly prone to landslides and slope instability due to its complex geology, geographic location, and periodic intense rainfall. These factors frequently trigger landslides, causing damage and disruption. Goal and Objectives The primary goal is to create a landslide susceptibility map to support effective land-use management and risk assessment. The objectives include: Identifying and analyzing key factors contributing to landslides. Utilizing landslide susceptibility mapping to guide risk management strategies.Minimizing the socio-economic impacts of terrain instabilities in Chefchaouen. Methodology A static method was used to assess nine conditioning factors for landslides: geology, slope, land use, altitude, aspect, proximity to roads and rivers, soil type, and precipitation. Data collection included a landslide inventory compiled by the urban municipality of Chefchaouen, high-resolution satellite imagery, and field verifications, identifying 400 instances of terrain instability. The dataset was split into two parts: 70% for training a predictive model and 30% for testing the model's performance using the AUC (Area Under Curve) metric. Slope exposure (orientation of a slope relative to geographic north) emerged as the factor with the highest prediction rate, at 8.711. Results The study produced a landslide susceptibility map that serves as a vital decision-making tool. This map highlights areas at risk and supports the development of risk management and land-use planning strategies, helping to mitigate the socio-economic consequences of terrain instability in Chefchaouen. Context and background In the Rif region of northern Morocco, terrain instabilities pose significant social, economic, and environmental challenges. The city of Chefchaouen, located in the heart of the Rif mountain range, is particularly prone to landslides and slope instability due to its complex geology, geographic location, and periodic intense rainfall. These factors frequently trigger landslides, causing damage and disruption. Goal and Objectives The primary goal is to create a landslide susceptibility map to support effective land-use management and risk assessment. The objectives include: Identifying and analyzing key factors contributing to landslides. Utilizing landslide susceptibility mapping to guide risk management strategies.Minimizing the socio-economic impacts of terrain instabilities in Chefchaouen. Methodology A static method was used to assess nine conditioning factors for landslides: geology, slope, land use, altitude, aspect, proximity to roads and rivers, soil type, and precipitation. Data collection included a landslide inventory compiled by the urban municipality of Chefchaouen, high-resolution satellite imagery, and field verifications, identifying 400 instances of terrain instability. The dataset was split into two parts: 70% for training a predictive model and 30% for testing the model's performance using the AUC (Area Under Curve) metric. Slope exposure (orientation of a slope relative to geographic north) emerged as the factor with the highest prediction rate, at 8.711. Results The study produced a landslide susceptibility map that serves as a vital decision-making tool. This map highlights areas at risk and supports the development of risk management and land-use planning strategies, helping to mitigate the socio-economic consequences of terrain instability in Chefchaouen. Context and background In the Rif region of northern Morocco, terrain instabilities pose significant social, economic, and environmental challenges. The city of Chefchaouen, located in the heart of the Rif mountain range, is particularly prone to landslides and slope instability due to its complex geology, geographic location, and periodic intense rainfall. These factors frequently trigger landslides, causing damage and disruption. Goal and Objectives The primary goal is to create a landslide susceptibility map to support effective land-use management and risk assessment. The objectives include: Identifying and analyzing key factors contributing to landslides. Utilizing landslide susceptibility mapping to guide risk management strategies.Minimizing the socio-economic impacts of terrain instabilities in Chefchaouen. Methodology A static method was used to assess nine conditioning factors for landslides: geology, slope, land use, altitude, aspect, proximity to roads and rivers, soil type, and precipitation. Data collection included a landslide inventory compiled by the urban municipality of Chefchaouen, high-resolution satellite imagery, and field verifications, identifying 400 instances of terrain instability. The dataset was split into two parts: 70% for training a predictive model and 30% for testing the model's performance using the AUC (Area Under Curve) metric. Slope exposure (orientation of a slope relative to geographic north) emerged as the factor with the highest prediction rate, at 8.711. Results The study produced a landslide susceptibility map that serves as a vital decision-making tool. This map highlights areas at risk and supports the development of risk management and land-use planning strategies, helping to mitigate the socio-economic consequences of terrain instability in Chefchaouen.

SHILAP Revista de lepidopterología
Abdelmalek Essaâdi University (MA)
Sustainable cities and communities
Openalex Percentile: Top 62%
Imbalanced Data Classification Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.