Deep Learning Based LSTM and SeqToSeq Models to Detect Monsoon Spells of India

  • Saicharan Viswanath
  • Moumita SahaEmail author
  • Pabitra Mitra
  • Ravi S. Nanjundiah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)


Monsoon spells are important climatic phenomenon modulating the quality and quantity of monsoon over a year. India being an agricultural country, identification of monsoon spells is extremely important to plan agricultural policies following the phases of monsoon to attain maximum productivity. Monsoon spells’ detection involve analyzing and predicting monsoon at daily levels which make it more challenging as daily-variability is higher as compared to monsoon over a month or an year. In this article, deep-learning based long short-term memory and sequence-to-sequence models are utilized to classify monsoon days, which are finally assembled to detect the spells. Dry and wet days are classified with precision of 0.95 and 0.87, respectively. Break spells are observed to be forecast with higher accuracy than the active spells. Additionally, sequence-to-sequence model is noted to perform superior to that of long-short term memory model. The proposed models also outperform traditional classification models for monsoon spell detection.


Active spell Break spell Long short term memory Sequence-to-sequence model Attention mechanism Classification Spells’ detection 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Saicharan Viswanath
    • 1
  • Moumita Saha
    • 2
    Email author
  • Pabitra Mitra
    • 3
  • Ravi S. Nanjundiah
    • 2
    • 4
    • 5
  1. 1.National Institute of Technology KarnatakaSurathkalIndia
  2. 2.Centre for Atmospheric and Oceanic SciencesIndian Institute of ScienceBangaloreIndia
  3. 3.Indian Institute of Technology KharagpurKharagpurIndia
  4. 4.Divecha Centre for Climate ChangeIndian Institute of ScienceBangaloreIndia
  5. 5.Indian Institute of Tropical MeteorologyPuneIndia

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