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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
MOHD HUSSEIN, SHAMSUL FAISAL
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting Faruq, Amrul; Marto, Aminaton; Izzaty, Nadia Karima; Kuye, Abidemi Tolulope; Mohd Hussein, Shamsul Faisal; Abdullah, Shahrum Shah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 1, February 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i1.1156

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.
Prediction of flood-affected areas based on geographic information system data using machine learning Faruq, Amrul; Syafaah, Lailis; Irfan, Muhammad; Abdullah, Shahrum Shah; Mohd Hussein, Shamsul Faisal; Yakub, Fitri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4675-4683

Abstract

Flood disasters have become more frequent and severe due to climate variability, posing significant threats to human lives, agriculture, and infrastructure. Effective disaster management and mitigation require accurate identification of flood-prone areas. This study develops an intelligent flood prediction system by integrating machine learning algorithms with geographic information systems (GIS) data to enhance flood risk assessment. The proposed system utilizes two machine learning models, including random forest (RF) and support vector machine (SVM), to predict flood-susceptible areas. The models are trained on historical flood data and GIS-derived features, including elevation, slope, topographic wetness index (TWI), aspect, and curvature. The dataset undergoes preprocessing, including normalization and feature selection, before being divided into training, validation, and test sets. The models are then trained and evaluated based on their predictive performance. Evaluation metrics, particularly the area under the curve (AUC), demonstrate that RF outperforms SVM in predicting flood-prone areas. RF achieves an accuracy of 82%, while SVM records a lower accuracy of 68%. The superior performance of RF is attributed to its ability to handle complex, nonlinear relationships in flood prediction. These results highlight the effectiveness of machine learning algorithms in flood susceptibility modeling and support the integration of data-driven techniques into flood and disaster risk reduction management strategies.
Co-Authors Aminaton Marto Amrul Faruq Fitri Yakub Izzaty, Nadia Karima Kuye, Abidemi Tolulope Muhammad Irfan Shahrum Shah Abdullah Syafaah, Lailis