Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/75612
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dc.contributor.authorNaruephorn Tengtrairaten_US
dc.contributor.authorWai Lok Wooen_US
dc.contributor.authorPhetcharat Parathaien_US
dc.contributor.authorChuchoke Aryupongen_US
dc.contributor.authorPeerapong Jitsangiamen_US
dc.contributor.authorDamrongsak Rinchumphuen_US
dc.date.accessioned2022-10-16T07:01:14Z-
dc.date.available2022-10-16T07:01:14Z-
dc.date.issued2021-07-05en_US
dc.identifier.issn14248220en_US
dc.identifier.other2-s2.0-85111831235en_US
dc.identifier.other10.3390/s21134620en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85111831235&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/75612-
dc.description.abstractSpatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations-Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectChemistryen_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.subjectPhysics and Astronomyen_US
dc.titleAutomated Landslide-Risk Prediction Using Web GIS and Machine Learning Modelsen_US
dc.typeJournalen_US
article.title.sourcetitleSensors (Basel, Switzerland)en_US
article.volume21en_US
article.stream.affiliationsPayap Universityen_US
article.stream.affiliationsUniversity of Northumbriaen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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