Combining Participatory Mapping, Cloud Computing and Machine Learning for Mapping Climate Induced Landslide Susceptibility in Lembeh Island, North Sulawesi
| العنوان: | Combining Participatory Mapping, Cloud Computing and Machine Learning for Mapping Climate Induced Landslide Susceptibility in Lembeh Island, North Sulawesi |
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| المؤلفون: | Endang Retnowati, Fakhrurrozi, Mikael Prastowo, Safran Yusri, Idris |
| المصدر: | IOP Conference Series: Earth and Environmental Science. 363:012020 |
| بيانات النشر: | IOP Publishing, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | business.industry, Elevation, Terrain, Landslide, Shuttle Radar Topography Mission, Machine learning, computer.software_genre, Random forest, Naive Bayes classifier, Kriging, Artificial intelligence, Digital elevation model, business, computer, Geology |
| الوصف: | This study explored the use of participatory mapping and several machine learning algorithms (Naïve Bayes, GMO Maxent, SVM, CART, and Random Forest) to map climate induced landslide susceptibility in Lembeh Island, North Sulawesi, based on Earth Observation data available in Google Earth Engine. Participatory mapping on landslide incidents were conducted in three villages, i.e., Kareko, Pintu Kota, and Pasir Panjang. Data used include digital elevation model from SRTM, multispectral imageries from Sentinel 2, and precipitation from CHIRPS. Terrain modelling was done to DEM to come up with elevation, slope, curvature, and aspect. A cloud free mosaic of Sentinel Images was created using the median reducer and then NDVI was calculated. Precipitation data from CHIRPS was sampled and interpolated using kriging and reduced to maximum and mean. Each algorithm was trained using 70% participatory mapping data and then the prediction was tested for accuracy using the last 30%. Results showed that Random Forest, SVM, CART, and GMO Maxent gave 0.98 testing accuracy and Naïve Bayes only 0.90. The map showed that due to the terrain condition, Lembeh Island is prone to Landslide and even though previously BNPB already provide a landslide hazard risk map, there were many areas not included on that map. Therefore, the map could become an input for BNPB and the Bitung City for developing a mitigation and adaptation strategy. Machine learning and cloud computing along with participatory mapping could also complement mechanistic or multi-criteria analysis using GIS model for landslide susceptibility mapping. |
| تدمد: | 1755-1315 1755-1307 |
| DOI: | 10.1088/1755-1315/363/1/012020 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::e669593e6e2ef6ece1e565fdc61c35f7 https://doi.org/10.1088/1755-1315/363/1/012020 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi...........e669593e6e2ef6ece1e565fdc61c35f7 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17551315 17551307 |
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| DOI: | 10.1088/1755-1315/363/1/012020 |