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Assessment of Flood Potential Through Rainfall Pattern Analysis

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Aditya Gupta
Vibha Jain
Published in
Data Science and Network Engineering
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the assessment of flood potential in India through the analysis of rainfall patterns using a deep learning-enabled Bidirectional Long Short-Term Memory (Bi-LSTM) model. The study utilizes a comprehensive dataset of historical rainfall data from 1901 to 2024, covering all Indian states, to identify rainfall patterns associated with flood-prone conditions. The Bi-LSTM model is developed to classify high-risk periods based solely on historical precipitation data, aiming to support more reliable flood prediction strategies. The performance of the Bi-LSTM model is evaluated and compared with traditional rainfall threshold approaches and other machine learning models. The results demonstrate the superior accuracy, precision, recall, and F1-score of the Bi-LSTM model, highlighting its effectiveness in capturing complex temporal relationships in rainfall data. This research provides valuable insights into enhancing disaster preparedness and climate resilience through advanced data-driven flood prediction methods.
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previous chapter Artificial Intelligence for Autism Detection Using EEG Signals and Lightweight Neural Networks
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Title
Assessment of Flood Potential Through Rainfall Pattern Analysis
Authors
Aditya Gupta
Vibha Jain
Copyright Year
2026
Book
Data Science and Network Engineering

Print ISBN: 978-3-032-07734-9

Electronic ISBN: 978-3-032-07735-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-07735-6_21
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