Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh
TL;DR: In this paper, three different Geographic Information System-based multi-criteria decision analysis methods were applied to scientifically assess the landslide susceptible areas in Chittagong Metropolitan Area (CMA), Bangladesh.
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Abstract: Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations.
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Global fatal landslide occurrence from 2004 to 2016
Abstract: . Landslides are a ubiquitous hazard in terrestrial environments with slopes,
incurring human fatalities in urban settlements, along transport corridors
and at sites of rural industry. Assessment of landslide risk requires
high-quality landslide databases. Recently, global landslide databases have
shown the extent to which landslides impact on society and identified areas
most at risk. Previous global analysis has focused on rainfall-triggered
landslides over short ∼ 5-year observation periods. This paper presents
spatiotemporal analysis of a global dataset of fatal non-seismic landslides,
covering the period from January 2004 to December 2016. The data show that in
total 55 997 people were killed in
4862 distinct landslide events. The spatial distribution of landslides
is heterogeneous, with Asia representing the dominant geographical area.
There are high levels of interannual variation in the occurrence of
landslides. Although more active years coincide with recognised patterns of
regional rainfall driven by climate anomalies, climate modes (such as El
Nino–Southern Oscillation) cannot yet be related to landsliding,
requiring a landslide dataset of 30 + years. Our analysis demonstrates that
landslide occurrence triggered by human activity is increasing, in particular
in relation to construction, illegal mining and hill cutting. This supports
notions that human disturbance may be more detrimental to future landslide
incidence than climate.
A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
TL;DR: The asymmetric and unsupervised FC-SAE can extract optimal non-linear features from environmental factors successfully, outperforms some conventional machine learning methods, and is promising for LSP.
383
Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
TL;DR: It can be inferred that the machine learning models have higher LSP performance than general statistical and heuristic models due to its high AUC accuracy and reasonable LSIs distribution features, while general statistical model is limited by its linear analysis and heuristics limited by subjective weighting process.
373
Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China
TL;DR: In this paper, the Particle Swarm Optimization and Support Vector Machine (PSO-SVM) coupling model based on the response of the induced factors was proposed to predict the landslide displacement.
312
Cities and climate change: Global report on human settlements 2011
Nathalie Ortar
- 17 May 2011
TL;DR: In this paper, the effects of urbanization and climate change are converging in dangerous ways that seriously threaten the world’s environmental, economic and social stability, and the United Nations report on Cities and Climate Change: Global Report on Human Settlements 2011 seeks to improve knowledge, among governments and all those interested in urban development and in climate change, on the contribution of cities to climate change.
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