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

Viswanath, S and Saha, M and Mitra, P and Nanjundiah, RS (2019) Deep Learning Based LSTM and SeqToSeq Models to Detect Monsoon Spells of India. In: 19th International Conference on Computational Science, ICCS 2019, 12 - 14 June 2019, Faro, pp. 204-218.

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Official URL: https://doi.org/10.1007/978-3-030-22741-8_15


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.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Verlag
Additional Information: The copyright for this article belongs to Springer Verlag.
Keywords: Agriculture; Atmospheric thermodynamics; Brain; Classification (of information); Deep learning, Active spell; Agricultural policies; Attention mechanisms; Break spell; Classification models; Maximum productivity; Sequence modeling; Short term memory, Long short-term memory
Department/Centre: Division of Mechanical Sciences > Centre for Atmospheric & Oceanic Sciences
Date Deposited: 05 Dec 2022 04:51
Last Modified: 05 Dec 2022 04:51
URI: https://eprints.iisc.ac.in/id/eprint/77935

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