Rain-triggered landslide hazard mapping in soft rock mountainous region : a case study of Chin State, Myanmar | |
| Author | Kyaw Swar Myint Thein |
| Call Number | AIT Diss. no.RS-23-04 |
| Subject(s) | Landslides--Burma--Inventories Landslide hazard analysis--Burma--Maps Landslides--Remote sensing |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Abstract | In 2015 late July, Hakha City,the Capital of Chìn State affected by serious landslides caused by high-intensity rainfall brought by a cyclone. Around 5000 people were affected and about 700 buildings were damaged. The aim of the study is to develop a rain-triggered landslide hazard map for Hakha City for future landslide mitigation The trend of the study is from landslide inventory, landslide susceptibility, and rain- triggered landslide hazard zonation mapping. The landslide inventory map is plotted with high-resolution images and field surveying. In the landslide inventory map,the spatial and temporal distribution of landslides with specific landslide types is included. The landslide types encountered in the study area are shallow slides, deep slides, slumps, and debris flow.A total landslide of 112 covered 3.62-kilometer square and it is 4.13% of the study area in the 2015 landslide event. Landslide susceptibility maps are generated by statistical modeling. The information value model and weight of evidence models are applied to evaluate the weight for dependent variables. In the statistical models, landslide inventory is used as an independent variable, and ten parameter maps, and possible causal factors for landslides are used as dependent variables.The map is generated by using average and maximum overlaying weighted parameter maps and the accuracy assessment is validated with under area curve statistics.The information value model has higher accuracy (80.67%) than that of weight of evidence model (80.04%). The regression analysis is applied for finding a relationship between landslides and rainfall. The shallow slides failed with daily rainfall and 2 days API whereas the deep landslides failed with 2 to 10 days API. The slumps start at 2 days API and best fit with 15 days API. The debris flow starts when a high magnitude in daily rainfall and 2 days API. Rain-triggered landslide hazard map is generated by simxulating rainfall and API to landslide susceptibility. The average o fdailv rainfall 2 days. 5 days, and 10 days are used to calculate rainfall indices for landslides and the observing method is used to validate the accuracy of landslide hazard prediction. According to the landslide data from 2016 and 2017, all the landslides belonged to high-risk zones, and the model is used as the landslide hazard map for Hakha City. |
| Year | 2023 |
| Type | Dissertation |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing and Geographic Information Systems (RS) |
| Chairperson(s) | Nagai, Masahiko; |
| Examination Committee(s) | Noppadol Phien-wej;Pal, Indrajit;Nakamura, Tai; |
| Scholarship Donor(s) | Government of Japan;AIT Fellowship; |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2023 |