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Machine learning

AI-Powered Rainfall Prediction System Developed by Chitkara University

Strategic Insights

— 1 min read

Researchers at Chitkara University in Himachal Pradesh, India, have developed an AI-powered system to predict rainfall using the MapReduce framework and convolutional long short-term memory (ConvLSTM) method. The innovative system, which combines adaptive salp-stochastic-gradient-descent-based ConvLSTM (adaptive S-SGD-based ConvLSTM), has demonstrated high prediction accuracy, achieving minimal values for Mean Square Error (MSE) and Percentage Root Mean Square Difference (PRD). This breakthrough has significant implications for Indian farmers, who rely heavily on agriculture for their livelihood. The system's ability to process large amounts of data and overcome computational limitations makes it an invaluable tool for making informed decisions about cultivation and irrigation.

Key Takeaways:

  • The AI-powered system, developed by Chitkara University researchers, utilizes the MapReduce framework and ConvLSTM method to predict rainfall.
  • The system uses an adaptive salp-stochastic-gradient-descent-based ConvLSTM (adaptive S-SGD-based ConvLSTM) to optimize the ConvLSTM model and achieve better prediction accuracy.
  • The system has demonstrated high prediction accuracy, achieving minimal values for Mean Square Error (MSE) and Percentage Root Mean Square Difference (PRD).
  • The system's ability to process large amounts of data and overcome computational limitations makes it an invaluable tool for making informed decisions about cultivation and irrigation.
  • The system's potential impact on Indian farmers who rely heavily on agriculture for their livelihood is significant.
  • The research team, led by Ashutosh Kumar Dubey, has published their findings in the International Journal of Interactive Multimedia and Artificial Intelligence.

Statistics:

  • Mean Square Error (MSE) of 0.0042
  • Percentage Root Mean Square Difference (PRD) of 0.8450
  • Accuracy of the adaptive S-SGD-based ConvLSTM system in comparing with previous approaches

Sources:

  • "An Adaptive Salp-stochastic-gradient-descent-based Convolutional Lstm With Mapreduce Framework for the Prediction of Rainfall." International Journal of Interactive Multimedia and Artificial Intelligence, 2025;9(4):32-44.
  • Chitkara University, Himachal Pradesh, India
  • NewsRx. New Artificial Intelligence Findings from Chitkara University Outlined (An Adaptive Salp-stochastic-gradient-descent-based Convolutional Lstm With Mapreduce Framework for the Prediction of Rainfall). Robotics & Machine Learning. October 27, 2025; p 238.

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