Enhancing River Flood Prediction in Early Warning Systems Using Fuzzy Logic-Based Learning

Rinta Kridalukmana; Dania Eridani; Risma Septiana; Ike Pertiwi Windasari

doi:10.46604/ijeti.2024.13426

Authors

Rinta Kridalukmana Department of Computer Engineering, Diponegoro University, Semarang, Indonesia https://orcid.org/0000-0003-3797-0134
Dania Eridani Department of Computer Engineering, Diponegoro University, Semarang, Indonesia
Risma Septiana Department of Computer Engineering, Diponegoro University, Semarang, Indonesia https://orcid.org/0000-0002-5139-9037
Ike Pertiwi Windasari Department of Computer Engineering, Diponegoro University, Semarang, Indonesia https://orcid.org/0009-0004-8687-1751

DOI:

https://doi.org/10.46604/ijeti.2024.13426

Keywords:

flood prediction, flood prevention, flood situation awareness, fuzzy logic-based learning

Abstract

Previous studies show that the fuzzy-based approach predicts incoming floods better than machine learning (ML). However, with numerous observation points, difficulties in manually determining fuzzy rules and membership values increase. This research proposes a novel fuzzy logic-based learning (FLBL) that embeds missing data imputations and a fuzzy rule optimization strategy to enhance ML performance while still benefiting from fuzzy theory. The simple moving average handles sensors’ missing data. The logical mapping is used for fuzzification automation and fuzzy rule generation. The join function between the Szymkiewicz–Simpson coefficient similarity and max function is applied to optimize a fuzzy rules model. The case study uses observation data from three rivers traversing three districts in Semarang City. As a result, FLBL achieves 97.87% accuracy in predicting flood, outperforming the decision tree (96%) and the neural network (73.07%). This work is significant as a part of preventive flood-related disaster plans.

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