Methods of building database to establish flooding map for coastal areas using a combination of artificial intelligence and GIS technology

  • Affiliations:

    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

  • *Corresponding:
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  • Received: 20th-Mar-2023
  • Revised: 23rd-July-2023
  • Accepted: 17th-Aug-2023
  • Online: 31st-Aug-2023
Pages: 12 - 21
Views: 698
Downloads: 44
Rating: 5.0, Total rating: 2
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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.

How to Cite
., T.Gia Nguyen, Nguyen, N.Viet, Pham, Q.Ngoc, , ., Nguyen, C.Van, Duong, Q.Anh, Nguyen, H.Dinh and Nguyen, N.Hoang 2023. Methods of building database to establish flooding map for coastal areas using a combination of artificial intelligence and GIS technology (in Vietnamese). Journal of Mining and Earth Sciences. 64, 4 (Aug, 2023), 12-21. DOI:

Akshayasimha Channarayapatna Harshasimha, Chandra Mohan Bhatt, (2023), Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam,

Environ. Sci. Proc. 2023, 25, 73.

Chau, V. N., Cassells, S., and Holland, J., (2015). Economic impact upon agricultural production from extreme flood events in Quang Nam, central Vietnam. Natural Hazards75, 1747-1765, DOI 10.1007/s11069-014-1395-x.

Costache, R., Țîncu, R., Elkhrachy, I., Pham, Q. B., Popa, M. C., Diaconu, D. C., ... and Bui, D. T., (2020a). New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping. Hydrological Sciences Journal65(16), 2816-2837, DOI: 10.1080/02626667.2020.1842412.

Costache, R., Popa, M. C., Bui, D. T., Diaconu, D. C., Ciubotaru, N., Minea, G., and Pham, Q. B., (2020b). Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning. Journal of Hydrology585, 124808,

Diaconu, D. C., Costache, R., and Popa, M. C., (2021). An Overview of Flood Risk Analysis Methods. Water 2021, 13, 474.

Dodangeh, E., Choubin, B., Eigdir, A. N., Nabipour, N., Panahi, M., Shamshirband, S., and Mosavi, A., (2020). Integrated machine learning methods with resampling algorithms for floodsusceptibility prediction. Science of the Total Environment705, 135983,

Mcleod, E., Poulter, B., Hinkel, J., Reyes, E., and Salm, R., (2010). Sea-level rise impact models and environmental conservation: A review of models and their applications. Ocean and Coastal Management53(9), 507-517.

Nghĩa, N. V., and Cường, N. C., (2020). Ứng dụng mạng Nơ-ron nhân tạo đa lớp trong thành lập mô hình phân vùng lũ quét khu vực miền núi Tây Bắc, thực nghiệm tại tỉnh Yên Bái. Tạp chí Khoa học Đo đạc và Bản đồ, (44), 56-64.

Pham, B. T., Luu, C., Van Phong, T., Nguyen, H. D., Van Le, H., Tran, T. Q., ... and Prakash, I., (2021). Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology592, 125815,

Rahman, M., Ningsheng, C., Islam, M. M., Dewan, A., Iqbal, J., Washakh, R. M. A., and Shufeng, T., (2019). Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Systems and Environment3, 585-601,

Luu, C., Von Meding, J., and Kanjanabootra, S. (2018). Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, Vietnam. Natural Hazards90, 1031-1050; DOI 10.1007/s11069-017-3083-0.

Luu, C., and Von Meding, J. (2018). A flood risk assessment of Quang Nam, Vietnam using spatial multicriteria decision analysis. Water10(4), 461; DOI:10.3390/w10040461.