Comparative Study of Rainfall Prediction Modeling Techniques (A Case Study on Srinagar, J&K, India)


  • Razeef Mohd Student, University of Kashmir, Jammu and Kashmir, India
  • Muheet Ahmed Butt Scientist-D, Department of Computer Science, University of Kashmir, Jammu and Kashmir, India
  • Majid Zaman Baba Scientist-D, Directorate of IT&SS, University of Kashmir, Jammu and Kashmir, India



Rainfall Prediction, Data Mining, J48, Random Forest, IBk, Naive Bayesian, Bagging


Prediction of rainfall is one of the most essential and demanding tasks for the weather forecasters since ages. Rainfall prediction plays an important role in the field of farming and industries. Precise rainfall prediction is vital for detecting the heavy rainfall and to provide the information of warnings regarding the natural calamities. Rainfall prediction involves recording the various parameters of weather like wind direction, wind speed, humidity, rainfall, temperature etc. From last few decades, it has been seen that data mining techniques have achieved good performance and accuracy in weather prediction than traditional statistical methods. This research work aims to compare the performance of few data mining algorithms for predicting rainfall using historical weather data of Srinagar, India, which is collected from website. From the collected weather data which comprises of 9 attributes, only 5 attributes which are most relevant to rainfall prediction are considered. Data mining process model is followed to obtain accurate and correct prediction results. In this paper, various data mining algorithms were explored which include decision tree based J48, Random forest, Naive Bayes, Bayes Net, Logistic Regression, IBk, PART and bagging. The experimental results show that J48 algorithm has good level of accuracy than other algorithms.


Olaiya, Folorunsho and Adesesan Barnabas Adeyemo, “Application of data mining techniques in weather prediction and climate change studies”, International Journal of Information Engineering and Electronic Business, Vol. 4, No. 1, pp. 51, 2012.

Sawaitul, D. Sanjay, K. P. Wagh and P. N. Chatur, “Classification and prediction of future weather by using back propagation algorithm-an approach”, International Journal of Emerging Technology and Advanced Engineering, Vol. 2, No. 1, pp. 110-113, 2012.

M. Kannan, S. Prabhakaran and P. Ramachandran, “Rainfall forecasting using data mining technique”, 2010.

Retrieved from

M. Kannan, S. Prabhakaran and P. Ramachandran, “Rainfall forecasting using data mining technique”, 2010.

Nikam, B. Valmik and B. B. Meshram, “Modeling rainfall prediction using data mining method: A Bayesian approach”, Computational Intelligence, Modelling and Simulation (CIMSim), 2013 Fifth International Conference on. IEEE, 2013.

Nhita, Fhira, “A rainfall forecasting using fuzzy system based on genetic algorithm”, Information and Communication Technology (ICoICT), 2013 International Conference of IEEE, 2013.

Mahajan, Seema and HimanshuMazumdar, “Rainfall Prediction using Neural Net based Frequency Analysis Approach”, International Journal of Computer Applications, Vol. 84, No. 9, 2013.

A. Geetha, and G. M. Nasira, “Data mining for meteorological applications: Decision trees for modeling rainfall prediction”, Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on. IEEE, 2014.

Dutta, Pinky Saikia and Hitesh Tahbilder, “Prediction of rainfall using data mining technique over Assam”, IJCSE, Vol. 5, No. 2, 2014, pp. 85-90.

Sharma, Ashutosh and Manish Kumar Goyal, “Bayesian network model for monthly rainfall forecast”, Research in Computational Intelligence and Communication Networks (ICRCICN), 2015 IEEE International Conference on IEEE, 2015.

Dubey and D. Akash, “Artificial neural network models for rainfall prediction in Pondicherry”, International Journal of Computer Applications, Vol. 120, No. 3, 2015.

Retrieved from


Ahmed, Bilal, “Predictive capacity of meteorological data: Will it rain tomorrow?”,Science and Information Conference (SAI), 2015, IEEE, 2015.

D. Hand, H. Mannila and P. Smyth, “Principles of data mining”, MIT, 2001.

Kotu, Vijay and BalaDeshpande, Predictive analytics and data mining: concepts and practice with rapidminer, Morgan Kaufmann, 2014.

T. Wang, W. Li, H. Shi and Z. Liu, “Software defect prediction based on classifiers ensemble”, Journal of Information & Computational Science, Vol. 8, No. 16, pp. 4241-4254, 2011.

I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, Los Altos, US, 2005.

R. O. Duda and P. E. Hart, Pattern classification and scene analysis, John Wiley and Sons, 1973.

P. Langley, W. Iba and K. Thompson, “An analysis of Bayesian Classifiers”, in Proceedings of the Tenth National Conference on Artificial Intelligence, San Jose, CA, 1992.

A. Mccallum and K. Nigam, “A Comparison of Event Models for Naive Bayes Text Classification”, Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98)-Workshop on Learning for Text Categorization, pp. 41-48, 1998.

T. Menzies, J. Greenwald and A. Frank, “Data Mining Static Code Attributes to Learn Defect Predictors”, IEEE Transactions on Software Engineering, Vol. 33, No. 1, 2-13, 2007.

L. Breiman, “Random forests”, Machine Learning, Vol. 45, No. 1, pp. 5-32, 2001.

L. Guo, Y. Ma, B. Cukic and H. Singh. Robust prediction of fault-proneness by random forests, In Proc. of the 15th International Symposium on Software Relaibility Engineering ISSRE’04, pp. 417-428, 2004.

Y. Jiang, B. Cukic, T. Menzies and N. Bartlow, “Comparing design and code metrics for software quality prediction”, Proc. Fourth Int. Workshop on Predictor Models in Software Engineering, PROMISE’08, New York, USA, 2008, pp. 11-18.

Retrieved from

D. Aha, “Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms”, Int. J. Man-Machine Studies, Vol. 36, 267-287, 1992.

R. Mohammad, M. Butt Ahmed and M. Baba Zaman, “Tools for Predictive Analytics : An Overview”, International Journal of Scientific Research Engineering & Technology (IJSRET), Vol. 6, No. 7, pp. 748-750, 2017.

R. M.Shah, M. A. Butt and M. Z. Baba, “Predictive Analytics Modeling: A Walkthrough”, Int. J. Adv. Res. Comput. Sci. Softw. Eng., Vol.7, No.6, pp. 421-426, June 2017.

R. M. Shah, M. A. Butt and M. Z. Baba, “ Review of Predictive Analytic Modeling techniques”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Vol. 6, No. 4, pp. 58-62, 2017.

R. Mohammad, M. Butt Ahmed and M. Baba Zaman, “Predictive Analytics: An Application Perspective”, International Journal of Computer Engineering and Applications, Vol. 9, No. 8, Aug. 2017.




How to Cite

Mohd, R., Ahmed Butt, M., & Baba, M. Z. . (2018). Comparative Study of Rainfall Prediction Modeling Techniques (A Case Study on Srinagar, J&K, India). Asian Journal of Computer Science and Technology, 7(3), 13–19.