Open AccessFlood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting
Author(s) -
Amrul Faruq,
Aminaton Marto,
Nadia Karima Izzaty,
Abidemi Tolulope Kuye,
Shamsul Faisal Mohd Hussein,
Shahrum Shah Abdullah
Publication year - 2021
Publication title -
kinetik
Language(s) - English
Resource type - Journals
eISSN - 2503-2267
pISSN - 2503-2259
DOI - 10.22219/kinetik.v6i1.1156
Subject(s) - adaptive neuro fuzzy inference system , flood forecasting , environmental science , warning system , flood myth , artificial neural network , flood warning , water level , hydrology (agriculture) , early warning system , normalization (sociology) , computer science , fuzzy logic , machine learning , artificial intelligence , engineering , fuzzy control system , geography , cartography , telecommunications , geotechnical engineering , archaeology , sociology , anthropology
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.