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2023 | OriginalPaper | Chapter

Annual Rainfall Prediction of Maharashtra State Using Multiple Regression

Authors : Loukik S. Salvi, Ashish Jadhav

Published in: Intelligent Computing and Networking

Publisher: Springer Nature Singapore

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Abstract

This paper presents a study of Indian rainfall and prediction of the annual rainfall in the state of Maharashtra and Konkan. The decreasing trends in seasonal rainfall and post-monsoon rainfall and increasing occurrence of the deficit rainfall years indicates the probable intensification of water scarcity in many states and sub divisions of India. Rainfall serves a major source of water only when it is conserved, thus a proper analysis and estimation of rainfall globally is of utmost importance. In an agricultural country like India, where the majority of agriculture is rain dependent rainfall prediction can help to understand the uncertainty in rainfall pattern which may affect the overall agricultural produce. The present study consists a descriptive analysis of annual rainfall in India from 1950–2020, this visualization may prove helpful for deciding the right model for prediction. This study is aimed at finding the most apt model for making accurate prediction for the rainfall dataset. Two machine learning model and a neural network model are implemented and their results are compared. The performance of the results was measured with MSE (mean squared error), RMSE (root mean square error), MAE (mean absolute error). The machine learning models showed high level of deviation as the time series data in use was highly inconsistent, on the other hand the neural network showed better efficiency due to the local dependency in the model which helps it to learn and perform better.

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Zhang X, Mohanty SN, Parida AK, Pani SK, Dong B, Cheng X (2020) Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha. In: IEEE access, vol 8, pp 30223–30233. https://doi.org/10.1109/ACCESS.2020.2972435

Khan MI, Maity R (2020) Hybrid deep learning approach for multi- step-ahead daily rainfall prediction using GCM simulations. IEEE Access 8:52774–52784. https://doi.org/10.1109/ACCESS.2020.2980977CrossRef

Sarma C (2022) Estimation and validation study of soil moisture using GPS-IR technique over a tropical region: variability of SM with rainfall and energy fluxes. IEEE J Sel Top Appl Earth Obs Remote Sens 15:42–49. https://doi.org/10.1109/JSTARS.2021.3127469CrossRef

Bhomia S, Jaiswal N, Kishtawal CM, Kumar R (2016) Multimodel prediction of monsoon rain using dynamical model selection. IEEE Trans Geosci Remote Sens 54(5):2911–2917. https://doi.org/10.1109/TGRS.2015.2507779CrossRef

Ratna SB (2012) Summer monsoon rainfall variability over Maharashtra, India. Pure Appl Geophys 169:259–273. https://doi.org/10.1007/s00024-011-0276-4

Gupta R et al (2021) WB-CPI: weather based crop prediction in India using big data analytics. In: IEEE access, vol 9, pp 137869–137885. https://doi.org/10.1109/ACCESS.2021.3117247

Tealab A et al (2017) Forecasting of nonlinear time series using ANN. Future Comput Inf J. https://doi.org/10.1016/j.fcij.2017.05.001

Sherpa SF, Shirzaei M, Ojha C, Werth S, Hostache R (2020) Probabilistic mapping of August 2018 flood of Kerala, India, using space-borne synthetic aperture radar. IEEE J Select Top Appl Earth Observ Remote Sens 13:896–913. https://doi.org/10.1109/JSTARS.2020.2970337CrossRef

Maitra A, De A, Adhikari A (2019) Rain and rain-induced degradations of satellite links over a tropical location. IEEE Trans Antennas Propag 67(8):5507–5518. https://doi.org/10.1109/TAP.2019.2911376

10.

Gopalakrishnan D, Chandrasekar A (2018) On the improved predictive skill of WRF model with regional 4DVar initialization: a study with north Indian ocean tropical cyclones. IEEE Trans Geosci Remote Sens 56(6):3350–3357. https://doi.org/10.1109/TGRS.2018.2798623

11.

Samsudin R, Shabri A, Saad P (2013) A comparison of time series forecasting using support vector machine and artificial neu- ral network model. J Appl Sci 10(11):950–958; Trivedi SK, Dey S (2013) Effect of various kernel sand feature selection methods on SVM performance for detecting email spams. Int J Comput Appl 66(21):18–23

12.

Üstün B, Melssen W, Buydens L (2006) Facilitating the application of support vector regression by using a universal Pearson VII function based kernel. Chemometr Intell Lab Syst 81(1):29–40; Paras SM, Kumar A, Chandra M (2009) A feature based neural network model for weather forecasting. Int J Comput Intell 4(3):209–216

13.

Mishra AK (2013) A new technique to estimate precipitation at fine scale using multifrequency satellite observations over indian land and oceanic regions. IEEE Trans Geosci Remote Sens 51(7):4349–4358. https://doi.org/10.1109/TGRS.2012.2226733CrossRef

14.

Kumarasiri AD, Sonnadara DUJ (2006) Rainfall forecasting: an artificial neural network approach. Proc Tech Sess 1–16

15.

Chattopadhyay S, Chattopadhyay M (2007) A soft computing technique in rainfall forecasting. arXiv:nlin/0703042 [online]. https://arxiv.org/abs/nlin/0703042; Deshpande RR (2012) On the rainfall time series prediction using multilayer perceptron artificial neural network. Int J Emerg Technol Adv Eng 2(1):2250–2459

16.

Ko M, Tiwari A, Mehnen J (2010) A review of soft computing applications in supply chain management. Appl Soft Comput 10:661–674

17.

Abraham ER, Mendes dos Reis JG, Vendrametto O, de Oliveira Costa Neto PL, Carlo Toloi R, de Souza AE, de Oliveira Morais M (2020) Time series prediction with artificial neural networks: an analysis using brazilian soybean production. Agriculture 10(10):475

18.

Salvi L, Patil H Data mining techniques and IoT used for building a futuristic water conservation system. IOSR J Eng (IOSRJEN). ISSN (e) 2250–3021. ISSN (p): 2278–8719

19.

Galit S, Koppius OR (2011) Predictive analytics in information systems research. Mis Quar 35(3):553–572

20.

Seber G, Lee AJ (2012) Linear regression analysis. Wiley Series in Probability and Statistics

21.

Montgomery DC, Peck EA, Vining GG (2015) Introduction to linear regression analysis. Wiley Series in Probability and Statistics

22.

Kavitha S, Varuna S, Ramya R (2016) A comparative analysis on linear regression and support vector regression. In: 2016 online international conference on green engineering and technologies (IC-GET), pp 1–5. https://doi.org/10.1109/GET.2016.7916627

23.

Wang Y, Du T, Liu T, Zhang L (2019) Dynamic multi-objective squirrel search algorithm based on decomposition with evolutionary direction prediction and bidirectional memory populations. IEEE Access 7:115997–116013. https://doi.org/10.1109/ACCESS.2019.2932883CrossRef

24.

Zhang X (2017) Prediction modeling of the population of preschool children based on logistic model. In: 2017 9th international conference on measuring technology and mechatronics automation (ICMTMA), pp 300–305. https://doi.org/10.1109/ICMTMA.2017.0079

25.

Ma W (2020) Prediction and analysis of population aging based on computer Leslie model. In: 2020 management science informatization and economic innovation development conference (MSIEID), pp 102–105. https://doi.org/10.1109/MSIEID52046.2020.00027

26.

Wang J, Ma X, Song R, Yan C, Luo H (2021) A novel disease prediction method based on inductive representation learning. In: 2021 IEEE 6th international conference on big data analytics (ICBDA), pp 239–243. https://doi.org/10.1109/ICBDA51983.2021.9403168

27.

Liu J, Zhang Y, Xiao Z, Qiao T, Tan H (2015) Fast, finite, accurate and optimal WASD neuronet versus slow, infinite, inaccurate and rough BP neuronet illustrated via russia population prediction. In: Sixth international conference on intelligent control and information processing (ICICIP), pp 140–145. https://doi.org/10.1109/ICICIP.2015.7388158

28.

Firmanuddin G, Supangkat SH (2016) City analytic development for modeling population using data analysis prediction. In: 2016 international conference on ICT for smart society (ICISS)

29.

Chakraborty S, Verma P, Paudel B, Shukla A, Das S (2022) Validation of synthetic storm technique for rain attenuation prediction over high-rainfall tropical region. In: IEEE geoscience and remote sensing letters, vol 19, pp 1–4. Art no 1002104. https://doi.org/10.1109/LGRS.2021.3068334

Title: Annual Rainfall Prediction of Maharashtra State Using Multiple Regression
Authors: Loukik S. Salvi
Ashish Jadhav
Publisher: Springer Nature Singapore
Book: Intelligent Computing and Networking
Print ISBN: 978-981-9900-70-1

Electronic ISBN: 978-981-9900-71-8

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-981-99-0071-8_14

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