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An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia

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Abstract

Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R 2), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor.

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Acknowledgments

This article is greatly benefited from very helpful reviews by two anonymous reviewers and editorial comments by James W. LaMoreaux.

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Authors and Affiliations

  1. Institute of Advanced Technology (ITMA), University Putra Malaysia (UPM), 43400, Serdang, Selangor Darul Ehsan, Malaysia

    Masoud Bakhtyari Kia, Biswajeet Pradhan & Ahmad Rodzi Mahmud

  2. Dezful Branch, Islamic Azad University, Dezful, Iran

    Saied Pirasteh

  3. Faculty of Environmental Studies, University Putra Malaysia, Serdang, Malaysia

    Wan Nor Azmin Sulaiman & Abbas Moradi

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  1. Masoud Bakhtyari Kia
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  2. Saied Pirasteh
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  3. Biswajeet Pradhan
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  4. Ahmad Rodzi Mahmud
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  6. Abbas Moradi
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Correspondence to Biswajeet Pradhan.

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Kia, M.B., Pirasteh, S., Pradhan, B. et al. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67, 251–264 (2012). https://doi.org/10.1007/s12665-011-1504-z

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