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Vol. 6, No. 1, February 2021

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Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

https://doi.org/10.22219/kinetik.v6i1.1156
Amrul Faruq
Universitas Muhammadiyah Malang
http://orcid.org/0000-0002-0702-668X
Aminaton Marto
Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia
https://orcid.org/0000-0003-3975-1269
Nadia Karima Izzaty
Indonesia Ministry of Public Works and Housing, Jakarta 12110
Abidemi Tolulope Kuye
UN-IHE Delft Institute for Water Education
Shamsul Faisal Mohd Hussein
Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia
https://orcid.org/0000-0003-0925-0906
Shahrum Shah Abdullah
Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia
https://orcid.org/0000-0001-5759-1241

Corresponding Author(s) : Amrul Faruq

faruq@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 6, No. 1, February 2021

Abstract

Intensively monitoring river water level and flows in both upstream and downstream catchments are essential for flood forecasting in disaster risk reduction. This paper presents a developed flood river water level forecasting utilizing a hybrid technique called adaptive neuro-fuzzy inference system (ANFIS) model, employed for Kelantan river basin, Kelantan state, Malaysia. The ANFIS model is designed to forecast river water levels at the downstream area in hourly lead times. River water level, rainfall, and river flows were considered as input variables located in upstream stations, and one river water level in the downstream station is chosen as flood forecasting point (FFP) target. Particularly, each of these input-output configurations consists of four stations located in different areas. About twenty-seven data with fifteen minutes basis recorded in January 2013 to March 2015 were used in training and testing the ANFIS network. Data preprocessing is done with feature reduction by principal component analysis and normalization as well. With more attributes in input configurations, the ANFIS model shows better result in term of coefficient correlation ( ) against artificial neural network (ANN)-based models and support vector machine (SVM) model. In general, it is proven that the presented ANFIS model is a capable machine learning approach for accurate forecasting of river water levels to predict floods for disaster risk reduction and early warning.

Keywords

Flood Forecasting ANFIS Machine Learning Disaster Risk Reduction Early Warning
Faruq, A., Marto, A., Izzaty, N. K., Kuye, A. T., Mohd Hussein, S. F., & Abdullah, S. S. (2021). Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(1), 1-10. https://doi.org/10.22219/kinetik.v6i1.1156
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