Skip to main content
SpringerLink
Log in

A Super Ensembled and Traditional Models for the Prediction of Rainfall: An Experimental Evaluation of DT Versus DDT Versus RF

  • Conference paper
  • First Online:
Communication and Intelligent Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 461))

  • 693 Accesses

Abstract

The main purpose of the current study is to use three traditional and ensemble machine learning approaches namely Decision tree (DT), Distributed Decision tree (DDT) and Random Forest (RF), and a classification tree model- Iterative Dicotomizer 3 (ID3) to predict the rainfall based on the historical data. A hard-voting classifier was used to check the accuracy performance in DDT and RF models. In this study, comparative performance of all the three techniques was assigned, and overall, all the three techniques perform reasonably well. Furthermore, it was observed that the DT model on the original dataset produces an accuracy of 81.70% followed by an accuracy of 81.41% in the case of the RF model. The overall performance in case of the DDT model got reduced to 78.46%. Thus, the obtained results predict that DT outperforms all other approaches with the highest accuracy measure and high susceptibility rate in rainfall prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zaman M, Kaul S, Ahmed M (2020) Analytical comparison between the information gain and Gini index using historical geographical data. Int J Adv Comput Sci Appl (IJACSA) 11(5):429–440

    Google Scholar 

  2. Zamani NW, Khairi SSM (2018, October) A comparative study on data mining techniques for rainfall prediction in Subang. In: AIP conference proceedings, vol 2013, No 1. AIP Publishing LLC, p. 020042

    Google Scholar 

  3. Safavin SR, Langrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674

    Article  MathSciNet  Google Scholar 

  4. Kapás Z, Lefkovits L, Szilágyi L (2016) Automatic detection and segmentation of brain tumor using random forest approach. In: Modeling decisions for artificial intelligence, Springer, Berlin, pp 301–312

    Google Scholar 

  5. Rokach L, Maimon O (2008) Data mining with decision trees: theory and applications. World Scientific Pub Co Inc.

    Google Scholar 

  6. Ashraf M, Zaman M, Ahmed M (2020) An intelligent prediction system for educational data mining based on ensemble and filtering approaches. Proc Comput Sci 167:1471–1483

    Article  Google Scholar 

  7. Zaman M, Quadri SMK, Butt MA (2012) Information translation: a practitioners approach. In: Proceedings of the World Congress on engineering and computer science, vol 1

    Google Scholar 

  8. Quinlan J (1999) Simplifying decision trees. Int J Hum Comput Stud 1999(51):497–491

    Article  Google Scholar 

  9. Zainudin S, Jasim DS, Bakar AA (2016) Comparative analysis of data mining techniques for malaysian rainfall prediction. Int J Adv Sci Eng Inform Technol 6(6):1148–1153

    Article  Google Scholar 

  10. Misra S, Li H (2019) Noninvasive fracture characterization based on the classification of sonic wave travel times. In: Machine learning for subsurface characterization, pp 243–287

    Google Scholar 

  11. Ashraf M, Ahmad SM, Ganai NA, Shah RA, Zaman M, Khan SA, Shah AA (2020) Prediction of cardiovascular disease through cutting-edge deep learning technologies: an empirical study based on Tensorflow, Pytorch and Keras. In: International conference on innovative computing and communications. Springer, Singapore, pp 239–255

    Google Scholar 

  12. Sarapardeh AH, Larestani A, Menad NA, Hajirezaie S (2020) Applications of artificial intelligence techniques in the petroleum industry. Gulf Professional Publishing

    Google Scholar 

  13. Shuja M, Mittal S, Zaman M (2020) Effective prediction of type II diabetes mellitus using data mining classifiers and SMOTE. In: Advances in computing and intelligent systems. Springer, Singapore, pp 195–211

    Google Scholar 

  14. Zaman EM, Quadri SMK, Butt EMA (2012) Information integration for heterogeneous data sources. IOSR J Eng 2(4):640–643

    Article  Google Scholar 

  15. Butt EMA, Quadri SMK, Zaman EM (2012) Star schema implementation for automation of examination records. In: Proceedings of the international conference on frontiers in education: computer science and computer engineering (FECS), p 1. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)

    Google Scholar 

  16. Chen J, Wang T, Abbey R, Pingenot J (2016) A distributed decision tree algorithm and its implementation on big data platforms. In: 2016 IEEE international conference on data science and advanced analytics (DSAA). IEEE, New York, pp 752–761

    Google Scholar 

  17. Desai A, Chaudhary S (2016) Distributed decision tree. In: Proceedings of the 9th Annual ACM India conference, pp 43–50

    Google Scholar 

  18. Bhaduri K, Wolff R, Giannella C, Kargupta H (2008) Distributed decision-tree induction in peer-to-peer systems. Stat Anal Data Min: ASA Data Sci J 1(2):85–103

    Article  MathSciNet  MATH  Google Scholar 

  19. Altaf I, Butt MA, Zaman M (2021) A pragmatic comparison of supervised machine learning classifiers for disease diagnosis. In: 2021 Third international conference on inventive research in computing applications (ICIRCA). IEEE, New York, pp 1515–1520

    Google Scholar 

  20. Altaf I, Butt MA, Zaman M (2022) Disease detection and prediction using the liver function test data: a review of machine learning algorithms. In: International conference on innovative computing and communications. Springer, Singapore, pp 785–800

    Google Scholar 

  21. Ashraf M, Zaman M, Ahmed M (2019) To ameliorate classification accuracy using ensemble vote approach and base classifiers. In: Emerging technologies in data mining and information security. Springer, Singapore, pp 321–334

    Google Scholar 

  22. Zaman M, Butt MA (2012) Information translation: a practitioners approach. In: World Congress on engineering and computer science (WCECS), San Francisco, USA. October, 2012

    Google Scholar 

  23. Butt MA, Quadri SMK, Zaman M (2012) Data warehouse implementation of examination databases. Int J Comput Appl 44(5):18–23

    Google Scholar 

  24. Fayaz SA, Altaf I, Khan AN, Wani ZH (2019) A possible solution to grid security issue using authentication: an overview. J Web Eng Technol 5(3):10–14

    Google Scholar 

  25. Aswini R, Kamali D, Jayalakshmi S, Rajesh R (2018) Predicting rainfall and forecast weather sensitivity using data mining techniques. Int J Pure Appl Math 119(14):843–847 ISSN: 1314-3395 (on-line version) http://www.ijpam.eu Special Issue

  26. Hemalatha P (2013) Implementation of data mining techniques for weather report guidance for ships using global positioning system. Int J Comput Eng Res 3(3)

    Google Scholar 

  27. Petre EG, A decision tree for weather prediction. BULETINUL UniversităŃii Petrol – Gaze din Ploieşti, vol LXI No. 1/2009 77 - 82 Seria Matematică - Informatică – Fizică

    Google Scholar 

  28. Ji S-Y, Sharma S, Yu B, Jeong DH (2012) Designing a rule-based hourly rainfall prediction model. In: 2012 IEEE 13th international conference on data analysis information reuse and integration (IRI); August 2012

    Google Scholar 

  29. Ashraf M, Zaman M, Ahmed M (2018) Performance analysis and different subject combinations: an empirical and analytical discourse of educational data mining. In: 2018 8th International conference on cloud computing, data science & engineering (confluence). IEEE, New York, pp 287–292

    Google Scholar 

  30. Mohd R, Butt MA, Baba MZ (2020) GWLM–NARX. Data technologies and applications

    Google Scholar 

  31. Irsoy O, Alpaydın E (2014) Distributed decision trees. arXiv preprint arXiv: 1412.6388

    Google Scholar 

  32. Zainudin S, Jasim DS, Bakar AA (2016) Comparative analysis of data mining techniques for Malaysian rainfall prediction. Int J Adv Sci Eng Inform Technol 6(6) ISSN: 2088-5334

    Google Scholar 

  33. Geetha A, Nasira GM (2014, December) Data mining for meteorological applications: decision trees for modeling rainfall prediction. In: 2014 IEEE international conference on computational intelligence and computing research. IEEE, New York, pp 1–4

    Google Scholar 

  34. Chao Z, Pu F, Yin Y, Han B, Chen X (2018) Research on real-time local rainfall prediction based on MEMS sensors. Hindawi J Sens 2018, Article ID 6184713, 9 p. https://doi.org/10.1155/2018/6184713

  35. Mohd R, Butt MA, Baba MZ (2018) SALM-NARX: Self adaptive LM-based NARX model for the prediction of rainfall. In: 2018 2nd international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC). IEEE, New York, pp 580–585

    Google Scholar 

  36. Fayaz SA, Zaman M, Butt MA (2021) To ameliorate classification accuracy using ensemble distributed decision tree (DDT) vote approach: an empirical discourse of geographical data mining. Proc Comput Sci 184:935–940

    Google Scholar 

  37. Ashraf M, Zaman M, Ahmed M (2018) Using ensemble Stackingc method and base classifiers to ameliorate prediction accuracy of pedagogical data. Proc Comput Sci 132:1021–1040

    Article  Google Scholar 

  38. Fayaz SA, Zaman M, Butt MA (2021) An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data. Int J Adv Technol Eng Explor 8(84):1424–1440. https://doi.org/10.19101/IJATEE.2021.874586

    Article  Google Scholar 

  39. Butt MA, Zaman M (2013) Assessment model based data warehouse: a qualitative approach. Int J Comput Appl 62(10). 16/j.scs.2020.102399

    Google Scholar 

  40. Fayaz SA, Zaman M, Butt MA (2022) Knowledge discovery in geographical sciences—a systematic survey of various machine learning algorithms for rainfall prediction. In: International conference on innovative computing and communications. Springer, Singapore, pp 593–608

    Google Scholar 

  41. Fayaz SA, Zaman M, Butt MA (2022) Performance evaluation of GINI index and information gain criteria on geographical data: an empirical study based on JAVA and python. In: International conference on innovative computing and communications. Springer, Singapore, pp 249–265

    Google Scholar 

  42. Fayaz SA, Zaman M, Butt MA (2022) Numerical and Experimental Investigation of Meteorological Data Using Adaptive Linear M5 Model Tree for the Prediction of Rainfall. Rev Comput Eng Res 9(1):1–12

    Google Scholar 

  43. Kaul S, Fayaz SA, Zaman M, Butt MA (2022) Is decision tree obsolete in its original form? A burning debate. Artif Intell Rev 36:105–113

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, University of Kashmir, Srinagar, India

    Sheikh Amir Fayaz & Muheet Ahmed Butt

  2. Directorate of IT & SS, University of Kashmir, Srinagar, India

    Majid Zaman

Authors
  1. Sheikh Amir Fayaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Majid Zaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muheet Ahmed Butt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Majid Zaman .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India

    Harish Sharma

  2. Institutional Area Narela Delhi, National Institute of Technology Delhi, New Delhi, India

    Vivek Shrivastava

  3. Design and Manufacturing, Indian Institute of Information Technology, Jabalpur, India

    Kusum Kumari Bharti

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fayaz, S.A., Zaman, M., Butt, M.A. (2022). A Super Ensembled and Traditional Models for the Prediction of Rainfall: An Experimental Evaluation of DT Versus DDT Versus RF. In: Sharma, H., Shrivastava, V., Kumari Bharti, K., Wang, L. (eds) Communication and Intelligent Systems . Lecture Notes in Networks and Systems, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-19-2130-8_48

Download citation

Publish with us

Policies and ethics

Search

Navigation