Paper Title : ARMA BASED CROP YIELD PREDICTION USING TEMPERATURE AND RAINFALL PARAMETERS WITH GROUND WATER LEVEL CLASSIFICATION

ISSN : 2394-2231
Year of Publication : 2022

10.5281/zenodo.6397053

Authors: R.Navin Kumar M.C.A.,M.Phil., V.Emayavarman

         



MLA Style: ARMA BASED CROP YIELD PREDICTION USING TEMPERATURE AND RAINFALL PARAMETERS WITH GROUND WATER LEVEL CLASSIFICATION " R.Navin Kumar M.C.A.,M.Phil., V.Emayavarman " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: ARMA BASED CROP YIELD PREDICTION USING TEMPERATURE AND RAINFALL PARAMETERS WITH GROUND WATER LEVEL CLASSIFICATION " R.Navin Kumar M.C.A.,M.Phil., V.Emayavarman " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
Nowadays, wireless telecommunication networks are promising alternative for rainfall measuring instruments that complement previous monitoring devices. Due to big dataset of the rainfall and the telecommunication networks data, empirical computational methods represnt less adequate of actual data. So, deep learning models are proposed for the analysis of big data and give more accurate presentation of real measurements. This project investigated rainfall monitoring results from experimental measurements. The main aim of this study is to provide a methodology for rainfall data classification based on neural network methods based on the historical rainfall data production data. Classification based on the previous years of rainfall can help farmers take necessary steps to measure crop production in the coming season. Understanding and assessing future crop production can help ensure food security and reduce impacts of climate change. In this work, ARMA (Auto Regressive Moving Average) method is used for proposed work. Past ten years of data set is taken for rainfall and ground water level for our country. The proposed work classifies the ground water level data set records using ARIMA model to predict the model for future test record data sets. The new model will helpf for analyzing ground water levels in past and so as to find the future levels

Reference
[1] P. Kumar "Crop yield forecasting by adaptive neuro fuzzy inference system." Mathematical Theory and Modeling 1.3, pp. 1- 7, 2011. [2] Sheikh Hefzul Bari. Md. Manjurul Hussain Shourav. “Forecasting Monthly Precipitation in Sylhet City Using ARIMA Model”. December 2015. Civil and Environmental Research. www.iiste.org ISSN 2224-5790 (Paper) ISSN 2225-0514 (Online) Vol.7, No.1, 2015. [3] S. Veenadhari, B. Misra, and C. D. Singh. "Machine learning approach for forecasting crop yield based on climatic parameters." 2014 International Conference on Computer Communication and Informatics, pp. 1-5. IEEE, 2014. [4] S. Prabakaran, P.N. Kumar, and P.S.M. Tarun. "Rainfall prediction using modified linear regression.", 2006. [5] Sundaram, S. Meenakshi, and M. Lakshmi. "Rainfall prediction using seasonal auto regressive integrated moving average model." Computer science 3, no. 4 (2014), pp. 58-60, 2014. [6] L. A. Zadeh, “Fuzzy sets,” Information and Control, pp. 338- 353, 1965. [7]Ehret, D. L., B. D. Hill, D. A. Raworth, and B. Estergaard. Artificial neural network modeling to predict cuticle cracking in green house peppers and tomatoes. Computers and Electronics in Agric. 61(2): 108‐116. 2008. [8]Pucheta, J., D. Patiño, R. Fullana, C.. Neurodynamic programming‐based optimal control for crop growth in precision agriculture. In Proc. 16th IFAC World Congress. Available at: www.nt.ntnu.no/users/skoge/prost/ proceedings/ ifac2005/ Papers/ Paper5047.html. Schugurensky, and B.Kuchen.2005. [9] HortiMax. 2008. Village Farms, USA, reports record tomato production. Rancho Santa Margarita, Cal.: HortiMax Growing Solutions. Schugurensky, and B.Kuchen.2005. [10] Ehrlich, H., M. Kühne, and J. Jäkel. Development of a fuzzy control system for greenhouses. Acta Hort. 406: 463‐470. 1996. [11]. Abdullah, A., Brobst, S, Pervaiz.I., Umer M.,and A.Nisar.2004. Learning dynamics of pesticide abuse through data mining. Proceedings of Australian Workshop on Data mining and Web Intelligence, New Zealand, January. [12] Kiran Mai,C., Murali Krishna, I.V., and A.VenugopalReddy,2006.Data Mining of Geospatial Database for Agriculture Related Application. Proceedings of Map India. New Delhi. ((http://www.gisdevelopment.net/proceedings/mapindia/2006/agri culture/mi06agri_12 4.htm). [13] Jorquera H, Perez R, Cipriano A, Acuna G(2001). Short term forecasting of air pollution episodes. In. Zannetti P (eds) Environmental Modeling 4. WITPress, UK. [14] Rajagopalan B. Lall U (1999) A k- nearest- neighbor daily precipitation and other weather variables. WatResResearch35(10) :3089 – 3101. [15] Tripathi S, Srinivas VV, Nanjudiah RS (2006).Down scaling of precipitation for climate change scenarios: a support vector machine approach, J. Hydrology 330-337. [16] Zelu Zia (2009). An Expert System Based on Spatial Data Mining used Decision Tree for Agriculture Land Grading. Second International Conference on Intelligent Computation Technology and Automation.Oct10-11,China [17] Jun Wu, Anastasiya Olesnikova, Chi- Hwa Song, Won Don Lee (2009).The Development and Application of Decision Tree for Agriculture Data. IITSI :16-20. [18] Dhanya,C.T. and D. Nagesh Kumar, 2009. Data mining for evolution of association rules for droughts and floods in India using climate inputs. J. of Geo. Phy.Res.114:1-15

Keywords
— Crop Yield Prediction, ARMA Model, KNN Classification, Neural Network.