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Floods are a recurring natural disaster in India, causing significant damage to life and infrastructure. This paper presents a machine learning-based approach to predict flood levels in already flooded areas, using image data from the ISRO Bhuvan website, weather data based on geographic coordinates, and elevation data. To get better accuracy of the proposed model, a convolutional neural network (CNN) is combined with the Long Short-Term Memory (LSTM) networks. A combination of CNN and LSTM is used to process the image and give it to LSTM along with numerical weather data. The experimental results show that the proposed model achieves an accuracy of 98% in the training data and 90% in testing data, which exhibits its potential to aid in flood management and mitigation.

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Enhancing Flood Prediction Accuracy Through LSTM-CNN Fusion Model with Satellite Imagery and Weather Data

  • 会议论文

  • 发表日期：

    2026年

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  • Amit Ruidas,
  • Harshit Kumar Sahu,
  • Asim Kumar Mahadani,
  • Pabitra Pal

摘要

Floods are a recurring natural disaster in India, causing significant damage to life and infrastructure. This paper presents a machine learning-based approach to predict flood levels in already flooded areas, using image data from the ISRO Bhuvan website, weather data based on geographic coordinates, and elevation data. To get better accuracy of the proposed model, a convolutional neural network (CNN) is combined with the Long Short-Term Memory (LSTM) networks. A combination of CNN and LSTM is used to process the image and give it to LSTM along with numerical weather data. The experimental results show that the proposed model achieves an accuracy of 98% in the training data and 90% in testing data, which exhibits its potential to aid in flood management and mitigation.