Abstract
Rainfall forecasting has been an onerous task to deal with. However, it is a part and parcel to sustain our life since it affects not only the agriculture growth but also the farming community. Rainfall prediction will definitely pose a great challenge but for meticulous planning and management of water resources. Therefore, this chapter presents an approach for rainfall prediction using Sliding Window concept with Jaccard distance metric measure. The Sliding Window Algorithm watches the information during a similar period in an earlier year and predicts precipitation in the next year. Using Sliding Window Algorithm, the precipitation expectation test was tested for Tirunelveli District, Tamil Nadu, India, using the rainfall data for a 10-year period.
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Abbreviations
- SWA:
-
Sliding window algorithm
- MSE:
-
Mean square error
- GRMSE:
-
Geometric root-mean-square error
- RMSE:
-
Root-mean-square error
- JD:
-
Joint director
- EY:
-
Earlier year
- PY:
-
Present year
- EV:
-
Earlier variation
- PV:
-
Present variation
- MPV:
-
Mean present variation
- MEV:
-
Mean earlier variation
- PRV:
-
Predicted variation
- AR:
-
Average rainfall
- FR:
-
Forecasted rainfall
References
Hirani D, Mishra N (2016) A survey on rainfall prediction techniques. Int J Comp Appl 6(2):28–42
Datar M, Gionis A, Indyk P, Motwani R (2002) Maintaining stream statistics over sliding windows. SIAM J Comp 31(6):1794–1813
Kapoor P, Bedi SS (2013) Weather forecasting using sliding window algorithm. Hindawi Publishing Corporation, ISRN Signal Processing, 2013, Article ID 156540.
Rajini Kanth TV, Balaram VVSSS, Rajasekhar N (2014) Analysis of Indian weather data sets using data mining techniques, pp. 89–94, © Cs & It-Cscp 2014
Viswambari M, Anbu Selvi R (2014, June) Data mining techniques to predict weather: a survey, ISSN 2348–7968, IJISET-Int J Innov Sci Eng Technol, 1(4)
Azahari F, Othman M, Saian R (2017) An enhancement of sliding window algorithm for rainfall forecasting. In 6th international conference on computing and informatics, 25–27
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Vijaya Chitra, M., Jacob, G. (2020). Using Sliding Window Algorithm for Rainfall Forecasting. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_32
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DOI: https://doi.org/10.1007/978-3-030-24051-6_32
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