Open Access

ARTICLE

Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

by G. Indra^1,*, N. Duraipandian²

1 Department of Computer Science and Engineering, RMK College of Engineering and Technology, Chennai, 601 206, India
2 Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, 602 106, India

* Corresponding Author: G. Indra. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1455-1470. https://doi.org/10.32604/iasc.2023.027703

Received 24 January 2022; Accepted 02 March 2022; Issue published 19 July 2022

Abstract

A flood is a significant damaging natural calamity that causes loss of life and property. Earlier work on the construction of flood prediction models intended to reduce risks, suggest policies, reduce mortality, and limit property damage caused by floods. The massive amount of data generated by social media platforms such as Twitter opens the door to flood analysis. Because of the real-time nature of Twitter data, some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue strategy. However, due to the shorter duration of Tweets, it is difficult to construct a perfect prediction model for determining flood. Machine learning (ML) and deep learning (DL) approaches can be used to statistically develop flood prediction models. At the same time, the vast amount of Tweets necessitates the use of a big data analytics (BDA) tool for flood prediction. In this regard, this work provides an optimal deep learning-based flood forecasting model with big data analytics (ODLFF-BDA) based on Twitter data. The suggested ODLFF-BDA technique intends to anticipate the existence of floods using tweets in a big data setting. The ODLFF-BDA technique comprises data pre-processing to convert the input tweets into a usable format. In addition, a Bidirectional Encoder Representations from Transformers (BERT) model is used to generate emotive contextual embedding from tweets. Furthermore, a gated recurrent unit (GRU) with a Multilayer Convolutional Neural Network (MLCNN) is used to extract local data and predict the flood. Finally, an Equilibrium Optimizer (EO) is used to fine-tune the hyperparameters of the GRU and MLCNN models in order to increase prediction performance. The memory usage is pull down lesser than 3.5 MB, if its compared with the other algorithm techniques. The ODLFF-BDA technique’s performance was validated using a benchmark Kaggle dataset, and the findings showed that it outperformed other recent approaches significantly.

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