Utilization of Open Source Spatial Data for Landslide Susceptibility Mapping at Chittagong District of Bangladesh—An Appraisal for Disaster Risk Reduction and Mitigation Approach

Since creation of spatial data is a costly and time consuming process, researchers, in this domain, in most of the cases rely on open source spatial attributes for their specific purpose. Likewise, the present research aims at mapping landslide susceptibility at the metropolitan area of Chittagong district of Bangladesh utilizing obtainable open source spatial data from various web portals. In this regard, we targeted a study region where rainfall induced landslides reportedly causes causalities as well as property damage each year. In this study, however, we employed multi-criteria evaluation (MCE) technique i.e., heuristic, a knowledge driven approach based on expert opinions from various discipline for landslide susceptibility mapping combining nine causative factors—geomorphology, geology, land use/land cover (LULC), slope, aspect, plan curvature, drainage distance, relative relief and vegetation in geographic information system (GIS) environment. The final susceptibility map was devised into five hazard classes viz., very low, low, moderate, high, and very high, representing 22 km2 (13%), 90 km2 (53%); 24 km2 (15%); 22 km2 (13%) and 10 km2 (6%) areas respectively. This particular study might be beneficial to the local authorities and other stake-holders, concerned in disaster risk reduction and mitigation activities. Moreover this study can also be advantageous for risk sensitive land use planning in the study area.

[1]  Ashish Pandey,et al.  Landslide Hazard Zonation using Remote Sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India , 2008 .

[2]  Daniela Richter,et al.  Classification of Landslide Susceptibility in the Development of Early Warning Systems , 2008, SDH.

[3]  P. Pasquali,et al.  Accuracy assessment of InSAR derived input maps for landslide susceptibility analysis: a case study from the Swiss Alps , 2005 .

[4]  A. Brenning Spatial prediction models for landslide hazards: review, comparison and evaluation , 2005 .

[5]  Fuad Mallick,et al.  Disaster Risk Reduction Approaches in Bangladesh , 2013 .

[6]  Saro Lee,et al.  Landslide susceptibility mapping using GIS and the weight-of-evidence model , 2004, Int. J. Geogr. Inf. Sci..

[7]  Soyoung Park,et al.  Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea , 2013, Environmental Earth Sciences.

[8]  John R. Argue,et al.  Variability of annual daily maximum rainfall of Dhaka, Bangladesh , 2014 .

[9]  C. Abdallah,et al.  Assessment of road instability along a typical mountainous road using GIS and aerial photos, Lebanon – eastern Mediterranean , 2001 .

[10]  Minoru Yamanaka,et al.  Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence , 2008 .

[11]  H. Aalders,et al.  Spatial Data Infrastructure , 2001 .

[12]  J. Voogd,et al.  Multicriteria evaluation for urban and regional planning , 1982 .

[13]  Fuchu Dai,et al.  Landslide risk assessment and management: an overview , 2002 .

[14]  M. K. Arora,et al.  An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas , 2004 .

[15]  B. Ahmed Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh , 2015, Landslides.

[16]  Filippos Vallianatos,et al.  Landslide hazard zonation in high risk areas of Rethymno Prefecture, Crete Island, Greece , 2010 .

[17]  A. Clerici,et al.  A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study (Italian Northern Apennines) , 2006 .

[18]  L. Tham,et al.  Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China , 2008 .

[19]  N. Karim Disasters in Bangladesh , 1995 .

[20]  I. Yilmaz Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .

[21]  R. Beighley,et al.  GIS‐based regional landslide susceptibility mapping: a case study in southern California , 2008 .

[22]  M. García-Rodríguez,et al.  Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression , 2008 .

[23]  R. Anbalagan,et al.  Landslide hazard evaluation and zonation mapping in mountainous terrain , 1992 .

[24]  S. Pascale,et al.  Neural networks and landslide susceptibility: a case study of the urban area of Potenza , 2008 .

[25]  M. Ercanoglu under a Creative Commons License. Natural Hazards and Earth System Sciences Landslide susceptibility assessment of SE Bartin (West Black Sea , 2022 .

[26]  C. Chung,et al.  Probabilistic prediction models for landslide hazard mapping , 1999 .

[27]  Shattri Mansor,et al.  Landslide susceptibility evaluation and factor effect analysis using Probabilistic-Frequency Ratio model , 2009 .

[28]  Manoj K. Arora,et al.  Impact of seismic factors on landslide susceptibility zonation: a case study in part of Indian Himalayas , 2010 .

[29]  R. Nagarajan,et al.  Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions , 2000 .

[30]  Ling Shi,et al.  Investigations and assessment of the landslide hazards of Fengdu county in the reservoir region of the Three Gorges project on the Yangtze River , 2004 .

[31]  M. Arora,et al.  An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas , 2005 .

[32]  C. F. Lee,et al.  Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong , 2002 .

[33]  Saro Lee,et al.  Earthquake-induced landslide-susceptibility mapping using an artificial neural network , 2006 .

[34]  Saro Lee,et al.  Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models , 2006 .

[35]  Mukta Sharma,et al.  Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[36]  Saro Lee Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS , 2004, Environmental management.

[37]  Anton Abdulbasah Kamil,et al.  Critical antecedent rainfall conditions for shallow landslides in Chittagong City of Bangladesh , 2012, Environmental Earth Sciences.

[38]  T. Korme,et al.  Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet Area, Ethiopia , 2001 .

[39]  Ming-Lang Lin,et al.  Statistical approach to earthquake-induced landslide susceptibility , 2008 .

[40]  H. Wang,et al.  Rainfall‐induced landslide hazard assessment using artificial neural networks , 2006 .

[41]  Minoru Yamanaka,et al.  DEM-Based Analysis of Earthquake-Induced Shallow Landslide Susceptibility , 2009 .

[42]  P. K. Champati ray,et al.  IRS-LISS-III and PAN data analysis for landslide susceptibility mapping using heuristic approach in active tectonic region of Himalaya , 2009 .

[43]  Bayes Ahmed,et al.  Landslide Inventory in an Urban Setting in the Context of Chittagong Metropolitan Area, Bangladesh , 2016 .

[44]  Meei-Ling Lin,et al.  A GIS-based potential analysis of the landslides induced by the Chi-Chi earthquake , 2004 .

[45]  W. Vahrson,et al.  Macrozonation Methodology for Landslide Hazard Determination , 1994 .

[46]  Ramesh P. Singh,et al.  Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data , 2006 .

[47]  Lucia Luzi,et al.  Slope Instability in Static and Dynamic Conditions for Urban Planning: the ‘Oltre Po Pavese’ Case History (Regione Lombardia – Italy) , 1999 .

[48]  Scott B. Miles,et al.  Rigorous landslide hazard zonation using Newmark's method and stochastic ground motion simulation , 1999 .

[49]  A. Günther,et al.  Combined rock slope stability and shallow landslide susceptibility assessment of the Jasmund cliff area (Rügen Island, Germany) , 2009 .

[50]  Biswajeet Pradhan,et al.  Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling , 2010, Environ. Model. Softw..

[51]  Methodology to evaluate rock slope stability under seismic conditions at Solà de Santa Coloma, Andorra , 2009 .

[52]  Alexander Strom,et al.  Analysis of landslide susceptibility in the Suusamyr region, Tien Shan: statistical and geotechnical approach , 2006 .

[53]  Saro Lee,et al.  Validation of an artificial neural network model for landslide susceptibility mapping , 2010 .

[54]  Saro Lee,et al.  Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system , 2011 .

[55]  Pinggen Zhou,et al.  GIS-Based and Data-Driven Bivariate Landslide-Susceptibility Mapping in the Three Gorges Area, China , 2009 .

[56]  Lewis A. Owen,et al.  GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region , 2008 .

[57]  Rex L. Baum,et al.  Transient deterministic shallow landslide modeling: Requirements for susceptibility and hazard assessments in a GIS framework , 2008 .