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Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest

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Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering (ICACTCE 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 735))

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Abstract

Climate change has made accurate rainfall forecasting more difficult than ever. In this paper, the decision tree algorithm and Random Forest is used to predict the rainfall accuracy based on historical climate data. The classification and regression tree (CART) approach is employed to this result, producing a better accuracy rate. The algorithm can determine the probabilities of rain on any given day, making it an ideal choice for various applications involving large datasets.

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References

  1. Appiah-Badu, N.K.A., Missah, Y.M., Amekudzi, L.K., Ussiph, N., Frimpong, T., Ahene, E.: Rainfall prediction using machine learning algorithms for the various ecological zones of Ghana. IEEE Access 10, 5069–5082 (2022). https://doi.org/10.1109/ACCESS.2021.3139312

    Article  Google Scholar 

  2. Biruntha, S., Sowmiya, B.S., Subashri, R., Vasanth, M.: Rainfall Prediction Using kNN and Decision Tree. In: Proceedings of the International Conference on Electronics and Renewable Systems, pp. 1757−1763. ICEARS. https://doi.org/10.1109/ICEARS53579.2022.9752220

  3. Prasad, N., Kumar, P., Naidu, M.M.: An Approach to Prediction of Precipitation Using Gini Index in SLIQ Decision tree. In: Proceedings-International Conference on Intelligent Systems, Modelling and Simulation, pp. 56–60. ISMS. https://doi.org/10.1109/ISMS.2013.27

  4. Prasad, N., Reddy, P.K., Naidu, M.M.: A Novel Decision Tree Approach for the Prediction of Precipitation Using Entropy in SLIQ. In: Proceedings-UKSim 15th International Conference on Computer Modelling and Simulation, pp. 209–216. UKSim 2013 (2013). https://doi.org/10.1109/UKSim.2013.74

  5. Shukla, B., Khatri, S. K., Kapur, P. K., Amity University, Amity University. Amity Institute of Information Technology, Computer Society of India., Institute of Electrical and Electronics Engineers. Uttar Pradesh Section, & Institute of Electrical and Electronics Engineers.: 2015 4th lnternational Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) : September 2–4, 2015. venue, Amity University Uttar Pradesh, Noida, India (n.d.-a).

    Google Scholar 

  6. Shukla, B., Khatri, S. K., Kapur, P. K., Amity University, Amity University. Amity Institute of Information Technology, Computer Society of India., Institute of Electrical and Electronics Engineers. Uttar Pradesh Section, & Institute of Electrical and Electronics Engineers.: 2015 4th lnternational Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) : September 2–4, 2015. venue, Amity University Uttar Pradesh, Noida, India (n.d.-b).

    Google Scholar 

  7. Tang, F., Wang, X., Wang, Z., Xiao, M., Ma, Y., Wan, Y.: Displacement Prediction of Rainfall Induced Landslide Based on AdaBoost BP Neural Network. In: 2022 3rd International Conference on Geology, Mapping and Remote Sensing, pp. 794–799. ICGMRS 2022 (2022). https://doi.org/10.1109/ICGMRS55602.2022.9849366

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Acknowledgements

This work was supported by Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education of Guizhou University (GZUAMT2022KF[07]), the National Natural Science Foundation of China (No.61862051), the Science and Technology Foundation of Guizhou Province (No.[2019]1299, No.ZK[2022]449), the Top-notch Talent Program of Guizhou province (No.KY[2018]080), the Natural Science Foundation of Education of Guizhou province (No.[2019]203) and the Funds of Qiannan Normal University for Nationalities (No. qnsy2019rc09). The Educational Department of Guizhou under Grant NO. KY[2019]067.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China

    Dan Wang

  2. School of Creative Technologies, University of Bolton, Bolton, BL3 5AB, UK

    Doyinsola Ayandiran, Chijioke Victor Uzochukwu & Celestine Iwendi

  3. LIM, Hassan II University of Casablanca, Casablanca, Morocco

    Zakaria Boulouard

  4. School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China

    Tao Hai

  5. School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang, China

    Tao Hai & Xiaofeng Ding

Authors
  1. Dan Wang
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  2. Tao Hai
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  3. Doyinsola Ayandiran
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  4. Chijioke Victor Uzochukwu
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  5. Xiaofeng Ding
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  6. Celestine Iwendi
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  7. Zakaria Boulouard
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Corresponding author

Correspondence to Doyinsola Ayandiran .

Editor information

Editors and Affiliations

  1. University of Bolton, Bolton, UK

    Celestine Iwendi

  2. Hassan II University Casablanca, El Mansouria, Morocco

    Zakaria Boulouard

  3. Department of Information Systems, Comenius University, Bratislava, Slovakia

    Natalia Kryvinska

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Wang, D. et al. (2023). Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest. In: Iwendi, C., Boulouard, Z., Kryvinska, N. (eds) Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering. ICACTCE 2023. Lecture Notes in Networks and Systems, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-031-37164-6_25

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