Methods of building database to establish flooding map for coastal areas using a combination of artificial intelligence and GIS technology
- Authors: Trong Gia Nguyen 1,2*, Nghia Viet Nguyen 1, Quang Ngoc Pham 1, 2, Cuong Van Nguyen 3, Quan Anh Duong 1, Hai Dinh Nguyen 4, Nhi Hoang Nguyen 5
1 Hanoi University of Mining and Geology, Hanoi, Vietnam
2 Geodesy and Environment research group, Hanoi University of Mining and Geology, Hanoi, Vietnam,
3 The Vietnam Agency of Seas and Islands, Hanoi, Vietnam
4 Nautical chart surveying and marine research team, Vietnamese People Navy, Haiphong, Vietnam
5 An Giang Construction and Traffic Consulting Joint Stock Company, Angiang, Vietnam
- Received: 20th-Mar-2023
- Revised: 23rd-July-2023
- Accepted: 17th-Aug-2023
- Online: 31st-Aug-2023
- Section: Geomatics and Land Administration
As a country with a coastline stretching from North to South, in recent years natural disasters, especially floods and inundation, have severely affected people and properties in Vietnam. In order to prevent and control natural disasters and adapt to climate change, there have been many researches to establish the flood-related map in the country. Among the methods of creating flood maps, the application of AI (Artificial Intelligence) combined with GIS (Geography Information System) has outstanding advantages due to its ability to handle a mixture of many types of input data in a geographical space unification. This method is also used widely in the world in general and Vietnam in particular. When applying the aforementioned method, building the input database of machine learning and artificial intelligence models is an essential issue. Based on the Sentinel-1, Landsat 8/9 images, digital elevation model (DEM), and soil maps, the authors have built the input database for modeling by using AI models. This paper introduces the method of building the input database for making flood maps using machine learning, and artificial intelligence combined with GIS. The computation process is divided into two steps: (1) Editing the component data layers from input data and (2) Standardization of data to transfer the component data layers into the same unit with the standard data format of Weka software. The research’s results are 11 data layers including the flood map in the past, elevation, slope, slope direction, curvature, terrain energy, geology, land use, soil, NDVI, NDWI for Quang Nam province.
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