Landslide hazard zonation assessment using GIS analysis at the coastal area of Safi (Morocco)

Bchari, Fatima El; Theilen‐Willige, Barbara; Malek, Halima Ait

doi:10.5194/ica-proc-2-24-2019

Cited by 12 publications

(3 citation statements)

References 22 publications

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“…Land-use is also an important factor for landslide initiation (Nourani et al 2013;Bchari 2019). This factor is considered as signi cant parameters in assessing landslide hazard mapping.…”

Section: Land-usementioning

confidence: 99%

GIS based Landslide Hazard Zonation Mapping by Weighted Overlay Method on the Road Corridor of North Sikkim Himalayas, India

Koley¹,

Nath²,

Bhattacharya³

et al. 2020

Preprint

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Landslide Hazards Zonation Mapping is a major tool for the geographer, geologist, ground engineer, and land-use planner for landslide prevention strategies. The main outcome of the present study is to prepare a Landslide Hazards Zonation Map of the region along the North Sikkim Road Corridor in Sikkim Himalayas, an area highly vulnerable to the landslides. The initial step of this study is involved preparation of input raster layers of the landslide controlling factors. Seven controlling factors are selected for this purpose. These controlling factor-like slope, aspect, lithology, faults, river alignment, road network, and land-use are through Geographic Information System software using multi-criteria analysis. The Analytic Hierarchy Process is used to determine the weightage of the various causative factors. Weighted Overlay Method is used for the assignment of ranks and weights to each factor. The Landslide Hazard Zonation Index is then estimated with the help of a multi-criteria analysis based on assigned rank and weight given by the Analytic Hierarchy Process. Finally, Landslide Hazard Zonation Mapping is done along the study area road corridor. Based on Hazards Index, the study area is classified into four hazard zones and classified as Very High (12.12%), High (40%), Moderate (37.20%), and Low Hazard Zones (10.68%). This zonation map is helpful for landslide hazard prevention, mitigation, proper planning of tourism and land-use management, and social development along the North Sikkim Road Corridor.

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“…Land-use is also an important factor for landslide initiation (Nourani et al 2013;Bchari 2019). This factor is considered as signi cant parameters in assessing landslide hazard mapping.…”

Section: Land-usementioning

confidence: 99%

GIS based Landslide Hazard Zonation Mapping by Weighted Overlay Method on the Road Corridor of North Sikkim Himalayas, India

Koley¹,

Nath²,

Bhattacharya³

et al. 2020

Preprint

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“…The occurrence of earth moving could be related to anthropogenic activities and natural factors. Landslides are considered one of the most devastating natural hazards and cause a significant loss of properties and human lives worldwide [2,3].…”

Section: Introductionmentioning

confidence: 99%

Development and Application of Models for Landslide Hazards in Northern Pakistan

Akbar

Ullah

et al. 2022

Sustainability

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In this paper, new models were investigated and developed for landslide hazards in Muzaffarabad District, located in the Azad Jammu and Kashmir region of Pakistan. The influential factors used in the landslide modelling were land use/landcover (LULC), elevation, slope, slope aspect, rainfall, drainage, road, surface roughness, and topographic index. The GIS-based Analytic Hierarchy Process (AHP) model was applied by utilizing the database of 35 active landslides and their pixels present in classes of all influential factors. The mean landslide hazard values, obtained from the mean landslide hazard analysis, were used as hazard weightages in the AHP model for development of a landslide hazard zone map. The highest mean hazard values for: (i) bare soil in LULC was 14.6%; (ii) 600–800 m in elevation was 6.89%; (iii) 30°–35° in slope was 6%; (iv) S and SW in slope aspect was 9.01%; (v) 1350–1405 mm/yr in rainfall was 9.03%; (vi) 40–80 m in buffered drainage was 12.83%; (vii) 40–80 m in buffered road was 12.48%; (viii) 60–138 in surface roughness index was 10.99%; (ix) −1.74–−1.25 in topographic position index was 13.07%. The percentages of very low, low, moderate, high, and very high landslide hazard zones were 1.48%, 11.80%, 39.36%, 37.36%, and 9.57% respectively. The co-efficient of the determination (r2) value of 0.96 indicated a strong relationship between the model development and validation. Thus, landslide hazard zone map models and methodology indicated a very high accuracy. This landslide hazard zone map could be utilized for the landslide damages’ reduction and the planning and development of road and building infrastructures in the study area. Additionally, this research could be replicated in other landslide prone areas of Pakistan for the minimizing the damages of landslides.

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“…The last decade has witnessed much progress in the modelling of landslide hazard maps, in particular owing to recent advances in artificial intelligence and its application to remote sensing and geoscientific research (Yesilnacar & Topal 2005;Pradhan et al 2010;Erener & Düzgün 2012;Kornejady et al 2017;Mirzaei et al 2018;Chen et al 2017a;Pandey et al 2020;Vakhshoori et al 2019). For instance, GIS-based multi-criteria decision-making approaches, such as Fuzzy Analytic Hierarchy Process (FAHP), have been applied to identifying areas susceptible to damaging landslides (Ercanoglu & Gokceoglu 2002;Gorsevski et al 2006;Gorsevski & Jankowski 2010;Vahidnia et al 2010;Pourghasemi et al 2012;Feizizadeh et al 2013;Tazik et al 2014;Roodposhti et al 2014;Feizizadeh et al 2014;Zhao et al 2017;El Bcharia et al 2019;Roy & Saha, 2019). Moreover, various machine learning algorithms, including support vector machine (SVM) (Pourghasemi & Kerle 2016;Youssef et al 2016;Pandy et al 2018), Maximum Entropy (MaxEnt) (Park, 2015;Kornejady et al 2017;Pandy et al 2018;Mokhtari & Abedian, 2019), Genetic Algorithm Rule-Set Production (GARP) (Stockwell, 1999;Rahmati et al 2019;Adineh et al 2018) and Random forest (RF) (Goetz et al 2015;Pourghasemi & Kerle 2016;Sevgen et al 2019;Pourghasemi et al 2020), and also, deep learning techniques including recurrent neural network (RNN) and Convolution Neural Networks (...…”

Section: Introductionmentioning

confidence: 99%

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

et al. 2021

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The accurate modelling of landslide risk is essential pre-requisite for the development of reliable landslide control and mitigation strategies. However, landslide risk depends on the poorly known environmental and socioeconomic factors for regional patterns of landslide occurrence probability and vulnerability, which constitute still a matter of research. Here, a hybrid model is described that couples data mining and multi-criteria decision-making methods for hazard and vulnerability mapping and presents its application to landslide risk assessment in Golestan Province, Northeastern Iran. To this end, landslide probability is mapped using three state-of-the-art machine learning (ML) algorithms -Maximum Entropy, Support Vector Machine and Genetic Algorithm for Rule Set Productionand combine the results with Fuzzy Analytical Hierarchy Process computations of vulnerability to obtain the landslide risk map. Based on obtained results, a discussion is presented on landslide probability as a function of the main relevant human-environmental conditioning factors in Golestan Province. In particular, from the response curves of the machine learning algorithms, it can be found that the probability of landslide occurrence decreases nearly exponentially with the distance to the next road, fault or river. Specifically, the results indicated that ≈ exp(− ), where the length-scale is about 0.0797 km − for road, 0.108 km − for fault and 0.734 km − for river. Furthermore, according to the results, follows, approximately, a lognormal function of elevation, while the equation = 0 − • ( − 0 ) 2 fits well the dependence of landslide modeling on the 2 slope-angle , with 0 ≈ 0.64, ≈ 25.6° and | | ≈ 6.6 × 10 −4 . However, the highest predicted landslide risk levels in Golestan Province are located in the south and southwest areas surrounding Gorgan City, owing to the combined effect of dense local human occupation and strongly landslide-prone environmental conditions.Obtained results provide insights for quantitative modelling of landslide risk, as well as for priority planning in landslide risk management.

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Landslide hazard zonation assessment using GIS analysis at the coastal area of Safi (Morocco)

Cited by 12 publications

References 22 publications

GIS based Landslide Hazard Zonation Mapping by Weighted Overlay Method on the Road Corridor of North Sikkim Himalayas, India

GIS based Landslide Hazard Zonation Mapping by Weighted Overlay Method on the Road Corridor of North Sikkim Himalayas, India

Development and Application of Models for Landslide Hazards in Northern Pakistan

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

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