Landslide Hazard Mapping Using a Poisson Distribution: A Case Study in Penang Island, Malaysia

Tay, Lea Tien; Lateh, Habibah; Hossain, Kamrul; Kamil, Anton Abdulbasah

doi:10.1007/978-3-319-05050-8_80

Cited by 18 publications

(6 citation statements)

References 9 publications

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“…The FR approach defines a quantitative relationship between landslide occurrence and spatial variability of a set of predictor variables [83]. This method is based on the calculation, for each class of the predictor variables, of the ratio of landslide area within that class to the total landslide area divided by the ratio of the area of that class to the total investigated area.…”

Section: Frmentioning

confidence: 99%

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Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

et al. 2019

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Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1,072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR–RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR–RF integrated model showed that the prediction accuracy of FR–RF–PALSAR (0.917) was higher than FR–RF–ASTER (0.865) and FR–RF–SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures.

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Section: Frmentioning

confidence: 99%

Section: Frmentioning

confidence: 99%

Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

et al. 2019

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“…The determination of subjective weight and objective weight helps to distinguish the contribution of these factors and analytic hierarchy process (AHP) and factor analysis (FA) are the two commonly used methods without prior conditions 49,50 . CF, FR and IV are three commonly used bivariate statistical methods which directly reveals the correlation between landslide locations and the influencing factors 51,52 . Besides, factors at different interval range have different susceptibility to landslide, and bivariate methods can distinguish the difference.…”

Section: Discussionmentioning

confidence: 99%

Application of an Integrated Model Based on Bivariate and Multivariate Method in Landslide Susceptibility Mapping

Wang

Zhu

2020

Preprint

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Landslides usually result in human losses and economic damages in mountainous areas especially for Himalayan areas. Landslide susceptibility mapping (LSM) is a key approach for avoiding hazard and risk. This study aims to explore an improved model combining multivariate and bivariate statistical methods for LSM. Four models were established as logistic regression (LR), LR integrated with certain factor (CF), LR integrated with frequency ratio (FR) and LR integrated with information value method (IV) and their performance was compared in LSM. Firstly, a landslide inventory map with 313 determined landslide events was prepared and 12 predisposing factors were selected. Secondly, the dataset was randomly divided into two parts, 75% of which was used for modeling and 25% for validation. Finally, area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. Results show that the performance of IVLR model is the best (AUC 0.792 and accuracy=78.8%). Besides, the LSM constructed by IVLR model did a reasonable job at predicting the distribution of susceptible areas. It identified the major factors and intervals of high susceptibility that profile curvature greater than 0.1, less than 2 km from the stream, maximum elevation difference greater than 1200 m and rainfall between 440 and 450 mm were prone to landslide. The conclusion reveals that the quality of LSM can be improved by comparing and combining the bivariate and multivariate methods, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.

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“…The FR is a primary statistical model that quanti es the relationship between the factor's classes within which the landslides have occurred and, accordingly, the relative rate of that particular class and the weights of the factors themselves. In general, the mathematics of Shannon entropy can be presented as follow (Tay et al, 2014;Sharma and Mahajan, 2019):…”

Section: Shannon Entropy Modelmentioning

confidence: 99%

Landslide susceptibility assessment and mapping using statistical and data mining models in Iran

Hanifinia

Nazarnejad

Najafi

et al. 2021

Preprint

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This study applied to evaluate landslide susceptibility using four data mining models including, “Generalized Linear Model (GLM)”, “Maximum Entropy (ME)”, “Artificial Neural Network (ANN)”, and “Support Vector Machine (SVM)” in Cherikabad Watershed in Urmia City, Iran. In particular, Shannon entropy was used to assess the intercomparison of factors’ classes. Eleven factors including, elevation, slope angle, slope aspect, geological formation, annual mean rainfall, land use/ land cover, distance to the village, distance to faults, distance to roads, distance to streams, and NDVI used in the current study. Landslide inventory map was identified using Google Earth imagery, extensive field surveys, and scrutinizing archived data. The produced landslide susceptibility maps were evaluated by the AUROC index. The results of performance metrics revealed that the Shannon entropy with an AUROC of 0.879 proved highly reliable and so is the intercomparison analysis of factors’ classes derived from it. Additionally, the goodness-of-fit of the GLM, ME, ANN, and SVM models were 0.763, 0.740, 0.926, and 0.924, while their predictive powers were 0.751, 0.727, 0.917, and 0.935, respectively. Hence, the results indicated that the SVM model can be introduced as the superior model for the study area based on which the most critical factors affecting landslides were found to be elevation, annual mean rainfall, and distance to the village. The results of this work are of great use for land use planning in landslide-prone areas with similar geo-topological, geomorphological, and climatic conditions.

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Landslide Hazard Mapping Using a Poisson Distribution: A Case Study in Penang Island, Malaysia

Cited by 18 publications

References 9 publications

Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

Application of an Integrated Model Based on Bivariate and Multivariate Method in Landslide Susceptibility Mapping

Landslide susceptibility assessment and mapping using statistical and data mining models in Iran

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