Volume 6, Issue 2, April 2020, Page: 69-71
Meteorological Data Analysis for Arid Region of Karnataka
Prajwala Talanki, Department of Computer Science, CMR institute of technology (VTU), Bangalore, Karnataka, India
Dassapa Ramesh, Department of Computer Applications, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India
Venugopal, Department of Computer Science, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India
Received: May 18, 2020;       Accepted: May 29, 2020;       Published: Jun. 15, 2020
DOI: 10.11648/j.ijdsa.20200602.12      View  311      Downloads  54
Meteorological data analysis is one of the time series prediction applications. Analysis of meteorological data give insights to the weather forecast and makes country more prepared for the worst situation like drought and flood. Northern part of Karnataka is usually a drought region. The paper provides insights into application of random forest and decision tree for a region of Karnataka called Raichur. The results of accuracy precision and recall are tabulated for Raichur region. There are 10 input features of climate considered in prediction of rainfall for a region. An accuracy of 96% is obtained after applying random forest to the meteorological data collected from IMD (Indian Meteorological Department). Raichur is an arid region of Karnataka which receives less rainfall. There were 13 input features considered for prediction of rainfall. The data was collected from Indian Meteorological Department (IMD) for a span of 17 years from January 1999 to December 2016 for prediction of rainfall. The decision tree classifier was applied to get an accuracy of 88%. The classification report shows a precision and recall of 0.90 and 0.97. Random forest an ensemble classifier was run through the dataset for an accuracy of 96%. The precision and recall of 1.00 and 0.99 was achieved. For both the algorithms a total of 11159 tuples were considered. There are total 11158 samples. The total training observations are 7810. The total testing samples are 3348. The decision rules are documented. Random forest algorithm shows a relative importance of parameters for Raichur rainfall prediction. A highest importance on rainfall prediction is Wet Bulb Temperature (WBT) and least important factor is Wind direction (FFF).
Random Forest Regressor, Gini Index, Classification Report, Decision Tree
To cite this article
Prajwala Talanki, Dassapa Ramesh, Venugopal, Meteorological Data Analysis for Arid Region of Karnataka, International Journal of Data Science and Analysis. Vol. 6, No. 2, 2020, pp. 69-71. doi: 10.11648/j.ijdsa.20200602.12
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
“Deep Learning based architecture for rainfall estimation integrating heterogeneous data sources”, Gianluigi Folino; Massimo Guarascio; Francesco Chiaravalloti; Salvatore Gabriele et. al, 2019 International Joint Conference on Neural Networks (IJCNN).
“Daily Rainfall Data Construction and Application to Weather Prediction” Choujun Zhan; Fujian Wu; Zhengdong Wu; Chi K. Tse et. al, 2019 IEEE International Symposium on Circuits and Systems (ISCAS).
Rainfall prediction based on 100 year meterological data”, sandeep kumar et. al, 2018 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN)”, feb 2018.
“Analyze the Rainfall of land slide on Apache Spark” Chou-yann-lee et. al IEEE conference on advanced computer Intelligence, march 2018.
“A Deep Neural Network Approach for Crop Selection and Yield Prediction in Bangladesh”, Tanhim Islam et. al, 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC).
“Rainfall Prediction: Accuracy Enhancement Using Machine Learning and Forecasting Techniques”, Urmay shah et. al, 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC).
“A Method of Rainfall Runoff Forecasting Based on Deep Convolution Neural Networks”, Xiaoli Li; Zhenlong Du; Guomei Song,, 2018 Sixth International Conference on Advanced Cloud and Big Data (CBD).
“Deep learning multilayer perceptron (MLP) for flood prediction model using wireless sensor network based hydrology time series data mining”, Indrastanti R. Widiasari et. al, 2017 International Conference on Innovative and Creative Information Technology (ICITech).
“Rainfall prediction of a maritime state (Kerala), India using SLFN and ELM techniques”, Yajnaseni Dash, et. al,: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) IEEE tansactions, April 2018.
“Early Prediction System Using Neural Network in Kelantan River, Malaysia” Mohd Azrol Syafiee Anuar* et. al, IEEE conference, 2017.
G. B. Huang, M. B. Li, L. Chen, C. K. Siew, “Incremental extreme learning machine with fully complex hidden nodes,” Neurocomputing, vol. 71 (x), pp. 576-583, 2008.
Browse journals by subject