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International Journal of Sensors, Wireless Communications and Control

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ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

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Research Article

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

Author(s): Pundra Chandra Shaker Reddy* and Alladi Sureshbabu

Volume 10, Issue 4, 2020

Page: [473 - 483] Pages: 11

DOI: 10.2174/2210327910666191218124350

Price: $65

Abstract

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall.

Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period.

Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models.

Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Keywords: Linear regression, hadoop, Map-Reduce, climate data, temperature, rainfall.

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[1]
Hossain I, Esha R, Alam IM. An attempt to use non-linear regression modelling technique in long-term seasonal rainfall forecasting for Australian Capital Territory. Geosciences (Basel) 2018; 8(8): 282.
[http://dx.doi.org/10.3390/geosciences8080282]
[2]
Anurag B, Manoj P, Vakeesh K, Pelash C. Weather Forecasting using Map-Reduce. Int J Innov Res Comput Commun Eng 2017; 5(9): 14945-52.
[3]
Reddy PC, Babu AS. Survey on weather prediction using big data analystics. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) Coimbatore, India 2017.
[http://dx.doi.org/10.1109/ICECCT.2017.8117883]
[4]
Senthil KM, Manikandan N, Senthil KU, Samy R. Weather data analysis using hadoop. Int J Pharm Technol 2016; 8(4): 21827-34.
[5]
Singhrattna N, Rajagopalan B, Clark M, Krishna KK. Seasonal forecasting of Thailand summer monsoon rainfall. Int J Climatol 2005; 25(5): 649.
[http://dx.doi.org/10.1002/joc.1144]
[6]
Joshi M, Shaikh S, Waghmode P, Mali P. Farmer buddy-weather prediction and crop suggestion using artificial neural network on map-reduce framework. Int J Comput Appl 2017; 159(7): 22-4.
[7]
Shabariram CP, Kannammal KE, Manojpraphakar T. Rainfall analysis and rainstorm prediction using MapReduce Framework. 2016 International Conference on Computer Communication and Informatics (ICCCI) Coimbatore, India 2016.
[http://dx.doi.org/10.1109/ICCCI.2016.7479954]
[8]
Xiaoyun Q, Xiaoning K, Chao Z, Shuai J, Xiuda M. Short-term prediction of wind power based on deep long short-term memory. 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) Xi'an, China 2015.
[http://dx.doi.org/10.1109/APPEEC.2016.7779672]
[9]
Reddy PC, Sureshbabu A. An adaptive model for forecasting seasonal rainfall using predictive analytics. Int J Intell Eng Syst 2019; 12(5): 22-32.
[10]
Ismail KA, Majid MA, Zain JM, Bakar NA. Big data prediction framework for weather temperature based on MapReduce algorithm. 2016 IEEE Conference on Open Systems (ICOS) Langkawi, Malaysia 2016.
[http://dx.doi.org/10.1109/ICOS.2016.7881981]
[11]
Feng Q, Wen X, Li J. Wavelet analysis-support vector machine coupled models for monthly rainfall forecasting in arid regions. Water Resour Manage 2015; 29(4): 1049-65.
[http://dx.doi.org/10.1007/s11269-014-0860-3]
[12]
Selvaragini S, Venkatesan E. Big data techniques for weather forecasting. Int J Pure Appl Math 2017; 116(18): 195-200.
[13]
Sharma V, Cali U, Hagenmeyer V, Mikut R, Ordiano JÁ. Numerical weather prediction data free solar power forecasting with neural networks. Proceedings of the Ninth International Conference on Future Energy Systems 2018.
[http://dx.doi.org/10.1145/3208903.3210279]
[14]
Navid MA, Niloy NH. Multiple linear regressions for predicting rainfall for bangladesh. Communications 2018; 6(1): 1-4.
[http://dx.doi.org/10.11648/j.com.20180601.11]
[15]
Ricciardelli E, Cersosimo A, Cimini D, et al. Analysis of heavy rainfall events occurred in Italy by using numerical weather prediction, microwave and infrared technique. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain 2018.
[16]
Nanda SK, Tripathy DP, Nayak SK, Mohapatra S. Prediction of rainfall in India using Artificial Neural Network (ANN) models. Int J Intell Syst Appl 2013; 5(12): 1.
[http://dx.doi.org/10.5815/ijisa.2013.12.01]
[17]
Reddyl B, Patil BA. Weather prediction based on big data using hadoop map reduce technique. Int J Adv Res Comput Commun Eng 2016; 5(6): 950-4.
[18]
Dagade V, Lagali M, Avadhani S, Kalekar P. Big data weather analytics using hadoop. Int J Emerg Technol Comput Sci Electron 2015; 2015: 976-1353.
[19]
Gad I. Big data techniques HADOOP and MAP reduce for weather Forecasting. International Journal of Latest Trends in Engineering and Technology 2016.
[20]
Fang W, Sheng VS, Wen X, Pan W. Meteorological data analysis using MapReduce. Sci World J 2014; 2014646497
[http://dx.doi.org/10.1155/2014/646497 ] [PMID: 24790576]
[21]
Feng QY, Vasile R, Segond M, et al. Climate learn: A machine-learning approach for climate prediction using network measures. Geosci Model Dev 2016; 2016: 2015-273.
[http://dx.doi.org/10.5194/gmd-2015-273]
[22]
Anjana J, Lakshmi M. Storm analysis with raw rainfall dataset by using artificial neural network and min-max algorithms. Indian J Sci Technol 2016; 10: 1-5.
[23]
Wu MC, Lin GF. The very short-term rainfall forecasting for a mountainous watershed by means of an ensemble numerical weather prediction system in Taiwan. J Hydrol 2017; 546: 60-70.
[http://dx.doi.org/10.1016/j.jhydrol.2017.01.012]
[24]
Corne D, Dissanayake M, Peacock A, Galloway S, Owens E. Accurate localized short term weather prediction for renewables planning. 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) Orlando, FL, USA 2014.
[http://dx.doi.org/10.1109/CIASG.2014.7011547]
[25]
Namitha K, Jayapriya A, Kumar GS. Rainfall prediction using artificial neural network on map-reduce framework. Proceedings of the Third International Symposium on Women in Computing and Informatics 2015.
[http://dx.doi.org/10.1145/2791405.2791468]
[26]
Suyatno JA, Nhita F, Rohmawati AA. Rainfall Forecasting in Bandung Regency Using C4.5 Algorithm. 6th International Conference on Information and Communication Technology (ICoICT). Bandung, Indonesia 2018.
[27]
Swain S, Patel P, Nandi S. A multiple linear regression model for precipitation forecasting over Cuttack district, Odisha, India. 2nd International Conference for Convergence in Technology (I2CT). Mumbai, India, 2017.

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