Delhi Weather Analysis : A Mongo Db Approach

Authors

  • Kazi AA Dept. of Computer, Rizvi College of Engineering, Mumbai University, Mumbai, Country
  • Shaikh S Dept. of Computer, Rizvi College of Engineering, Mumbai University, Mumbai, Country
  • Shaikh S Dept. of Computer, Rizvi College of Engineering, Mumbai University, Mumbai, Country
  • Shaikh S Dept. of Computer, Rizvi College of Engineering, Mumbai University, Mumbai, Country

DOI:

https://doi.org/10.26438/ijcse/v7i10.156158

Keywords:

MongoDb, Weather, Analysis, Queries

Abstract

The application of science and technology in predicting the weather of a given area is weather forecasting. The whole world is experiencing extreme climatic change which causes side effects .In order to reduce these side effects we use mathematical algorithms and techniques on big data of weather data to analyse the current situation and predict the future weather conditions. In this research we will use be using Mongo DB to analyse the data on weather in Delhi. The outcomes shows us the analysis of the weather data available.

References

[1] Garima Jain1 , Bhawna Mallick2 Student, “A Review on Weather Forecasting Techniques”, International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 5, Issue 12, December 2016 Department of Computer Science and Engineering, Galgotias Educational Institutions, Greater Noida, Uttar Pradesh, India1 Head of Department (Computer Science), Galgotias Educational Institutions, Greater Noida, Uttar Pradesh, India.

[2] Sushmitha Kothapalli, S. G. Totad, “A Real-Time Weather Forecasting and Analysis”, IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017), pp 1567- 1570

[3] Tiwari, R. Sam and S. Shaikh, "Analysis and prediction of churn customers for telecommunication industry," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 218-222. doi: 10.1109/I-SMAC.2017.8058343

[4] S. Navadia, P. Yadav, J. Thomas and S. Shaikh, "Weather prediction: A novel approach for measuring and analyzing weather data," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 414-417. doi: 10.1109/I-SMAC.2017.8058382

[5] S. Shaikh, S. Rathi and P. Janrao, "IRuSL: Image Recommendation Using Semantic Link," 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN), Tehri, 2016, pp. 305-308. doi: 10.1109/CICN.2016.66

[6] S. Shaikh, S. Rathi and P. Janrao, "Recommendation System in E-Commerce Websites: A Graph Based Approached," 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 931-934. doi: 10.1109/IACC.2017.0189

[7] Farhad Soleimanian Gharehchopogh, Tahmineh Haddadi Bonaband Seyyed Reza Khaze, “ A linear regression approach to prediction of stock market trading volume: a case study” Vol.4, No. 3, September 2013, International Journal of Managing Value and Supply Chains (IJMVSC).

[8] Behrouz Minaei-Bidgoli, Deborah A. Kashy, Gerd Kortemeyer , William F. Punch, “Predicting student performance: an application of data mining methods with the educational web-based system lon-capa”, November 5- 8, 2003, Boulder, CO 33rd ASEE/IEEE Frontiers in Education Conference,ISSN: 0-7803-7444-4/03/$17.00

[9] Ranzato , M., Y., Boureau, 1.., Chopra, S., &LeCun, Y. "A unified energy-based framework for unsupervised learning," In Proc.Conference on AI and Statistics (AIStats), vol. 20, 2007.

[10] Linkon Chowdhury , Md.Sarwar Kamal & Sonia Farhana Nimmy, “Artificial System to Compare Energy Status in the Context of Europe Middle East”, Global Journal of Computer Science and Technology Volume 12 Issue 8 Version 1.0 ,April 2012, pp25-30

[11] Han, J., Kamber, M.: “Data Mining Concepts and Techniques”, Morgan Kaufmann Publishers, 2006.

[12] A. aGautm and P. Bedi, "MR-VSM: Map Reduce based vector SpaceModel for user profiling-an empirical study on News data," 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, 2015, pp. 355-360.

[13] Agrawal, R., Jain, R.C., Jha, M.P. and Singh, D. (1980): Forecasting of rice yield using climatic variables. Indian Journal of Agricultural Science, Vol. 50, No. 9, pp. 680-684.

[14] Lee, S., Cho, S.& Wong, P.M., (1999) : Rainfall prediction using artificial neural network.― J. Geog. Inf. Decision Anal. 2, 233–242 1998. [10] Wong, K. W., Wong, P. M., Gedeon, T. D. & Fung, C. C. ―Rainfall Prediction Using Neural Fuzzy Technique.

[15] C. Hamzacebi, “Improving artificial neural networks’ performance in seasonal time Series Forecasting”, Information Sciences 178 (2008), pages: 4550-4559.

Downloads

Published

2019-10-31
CITATION
DOI: 10.26438/ijcse/v7i10.156158
Published: 2019-10-31

How to Cite

[1]
A. A. Kazi, S. Shaikh, S. Shaikh, and S. Shaikh, “Delhi Weather Analysis : A Mongo Db Approach”, Int. J. Comp. Sci. Eng., vol. 7, no. 10, pp. 156–158, Oct. 2019.

Issue

Vol. 7 No. 10 (2019): IJCSE October Edition

Section

Survey Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.