Paper Title : Meteorological Data Analysis of Semi Arid Region Of Karnataka Using Relative Importance of Features and Adaptive Boosting classifier
ISSN : 2394-2231
Year of Publication : 2020
MLA Style: Prajwala T R, Dr D Ramesh, Dr H Venugopal " Meteorological Data Analysis of Semi Arid Region Of Karnataka Using Relative Importance of Features and Adaptive Boosting classifier " Volume 7 - Issue 5 September - October,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: Prajwala T R, Dr D Ramesh, Dr H Venugopal " Meteorological Data Analysis of Semi Arid Region Of Karnataka Using Relative Importance of Features and Adaptive Boosting classifier " Volume 7 - Issue 5 September - October,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
- Meteorological data analysis is obtaining the information from raw data. There is vast amount of data available for weather analysis. Market needs timely and accurate data. The collection and datawarehouse of weather data is important because it provides an economic benefit but the local or national economic needs are not as dependent on high data quality as is the weather risk market. The semi arid region of Karnataka namely Madikeri region is considered for data analysis. The relative importance of features is identified for analysis of rainfall data. The adaptive boosting random forest classifier is applied to generate decision rules governing the prediction of rainfall. The data is collected from Indian Meteorological Department (IMD) for span of 12 years from 2004 to 2016. There are 4825 samples considered for the data analysis. The number of features considered for data analysis is 13 for prediction of rainfall. The validation curve and RMSE values justify the results obtained.
1. Indian Metrological department IMD http://dsp.imdpune.gov.in/ 2. xiaobo zhang et.al, “Annual and Non-Monsoon Rainfall Prediction Modelling Using SVR-MLP: An Empirical Study From Odisha”, :Fourth International Conference on Computing Method- ologies and Communication, Vol 8, pp. 1302-1310,. 2020 3. hattopadhyay, et.al Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data. Sci Rep 10, 1317 (2020). https://doi.org/10.1038/s41598-020-57897-9,2020 4. Min Min , Chen Bai, et.al “Estimating Summertime Precipitation from Himawari-8 and Global Forecast System Based on Machine Learning”, IEEE transactions on geoscience and remote sensing, vol. 57, no. 5,pp.2557-2565 ,May 2019 5. C.P. Shabariram et.al “Rainfall Analysis and Rainstorm prediction using mapreduce framework”, International Conference on Computer Communica- tion and Informatics (ICCCI), vol34, no 7 pp.657-701,2019 6. Ali P.Yunusb DieuTien et.al “Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan”, Science of The Total Environment Volume 662, 20 April 2019, Pages 332-346 7. KamrulHasan et.al, “Comparison between meteorological data and farmer perceptions of climate change and vulnerability in relation to adaptation”, Journal of Environmental Management Elsevier, Volume 237, 1 May 2019, Pages 54-62 8. Dang, V., Dieu, T.B., Tran, X. et al. Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier. Bull Eng Geol Environ 78, 2835–2849 (2019). https://doi.org/10.1007/s10064-018-1273-y 9. Gurpreet Singh et.al,” Hybrid Prediction ModelsForRainfallForecasting”, :International Conference on Inventive Research in Computing Applications (ICIRCA) 2019 10. Parikshit Kishor et.al,” Comparative Study of Neural Network Architectures”, IEEE Technological Innovations in ICT for Agriculture and Rural Development, Third International Conference on Computing Methodologies and Communication vol3,no2,pp472-479 ,2019 11. Dr. Rupali Patil. et.al,” IOT Based Rainfall Monitoring System Using WSN En- abled Architecture”,vol 4 no 2 pp 235-240, 2019 12. Zhou, K., Zheng, Y., Li, B. et al. Forecasting Different Types of Convective Weather: A Deep Learning Approach. J Meteorol Res 33, 797–809 (2019). https://doi.org/10.1007/s13351-019-8162-6,2019 13. KhabatKhosraviaPrasad, DaggupatiaMohammadet.al, “Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq”, Computers and Electronics in Agriculture Elsevier ,Volume 167, December 2019, 105041. 14. Tyralis, H.; Papacharalampous, G.; Langousis, A. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources. Water 2019, 11, 910.
relative Feature importance, adaptive boost random classifier, RMSE, validation curve and classification report