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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Rissal Efendi
Satya Wacana Christian University
Computer Science & IT Electrical & Electronics Engineering

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Integrating hybrid deep learning and CSI for multi-interval hydrological data in enhanced flood prediction Indrastanti Ratna Widiasari; Eko Sediyono; Rissal Efendi
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11278

Abstract

Flood prediction accuracy is often constrained by heterogeneous and asynchronous hydrological data collected at different time intervals. This study proposes a hybrid deep learning–based flood prediction framework that integrates long short-term memory (LSTM), convolutional neural network (CNN), and cubic spline interpolation (CSI) to address these challenges. Rainfall, river discharge, and water level data representing upstream, midstream, and downstream conditions of the Bengawan Solo watershed were utilized. CSI was applied as a preprocessing step to harmonize multi-interval data, reduce noise, and recover missing observations, thereby improving data consistency. The experimental results show that the proposed hybrid LSTM–CNN model enhanced with CSI outperforms baseline LSTM and non-interpolated hybrid models, achieving a mean absolute percentage error (MAPE) of 5.84%, root mean square error (RMSE) of 0.125 m, mean absolute error (MAE) of 0.082 m, and R² of 0.948. The integration of spatio-temporal feature learning with data harmonization enables more accurate flood level prediction and supports timely flood early warning systems. The proposed approach demonstrates strong potential for improving flood risk management and disaster preparedness in flood-prone regions.
Real-time flood forecasting with attention-enhanced hybrid deep learning using internet of things data Rissal Efendi; Indrastanti R. Widiasari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27501

Abstract

Floods are a frequent disaster in Semarang city, Indonesia, requiring an accurate and real-time forecasting system to support effective risk management. This study introduces a hybrid long short-term memory-gated recurrent unit (LSTM-GRU) model with an attention mechanism (attention-enhanced LSTM-GRU) designed to improve the accuracy of flood predictions based on multiparameter internet of things (IoT) data. The novelty of this study lies in the integration of the attention mechanism within the hybrid LSTM-GRU architecture, which allows the model to provide adaptive focus on features and time periods that most influence flood occurrences. The dataset used consists of 1,736 time series samples covering rainfall and water level data collected every 15 minutes from IoT sensors in the upstream and downstream areas of Semarang, Indonesia. Experimental results show that the hybrid model with the attention mechanism provides the best performance with a mean absolute percentage error (MAPE) value of 1.4%, root mean squared error (RMSE) of 1.05, and coefficient of determination (R²) reaching 0.96. This model also achieves 100% recall for the “Danger” class, demonstrating its reliability in detecting critical conditions. The practical implication of this research is the availability of a flood prediction model that is accurate, adaptive, and can be directly applied to IoT-based early warning systems in flood-prone urban areas.
Co-Authors Eko Sediyono Indrastanti R. Widiasari Indrastanti Ratna Widiasari