Rainfall Based Flood Prediction in Kerala Using Machine Learning

Authors

  • Vidya S Associate Professor, Sri Sai Ram Institute of Technology, Chennai, India
  • Gayathri S U.G. Student, Sri Sai Ram Institute of Technology, Chennai, India
  • Dilsha M U.G. Student, Sri Sai Ram Institute of Technology, Chennai, India
  • Krithika S U.G. Student, Sri Sai Ram Institute of Technology, Chennai, India

Keywords:

Deep Learning, Flood Prediction, Hydrological model, Machine Learning, Statistical analysis

Abstract

Among the most damaging natural disasters are floods, which are very difficult to model. Flood prediction is a critical task that involves forecasting the likelihood of floods in a given area, allowing people to take necessary precautions to minimise damages and prevent loss of life. Machine learning (ML) algorithms have shown great potential in flood prediction, as they can analyse large amounts of data from multiple sources to provide accurate and timely predictions. The objective is to find a prediction model that is more accurate and efficient by incorporating new machine learning techniques and hybridising current ones. Both hydrologists and climate scientists can use this model as a guidance for selecting the appropriate machine learning technique for a given prediction problem. The output of the ML-based flood prediction system can also be integrated with existing flood warning systems, enabling authorities to send out alerts in a timely manner and take necessary precautions to minimise the effects of flooding.

Downloads

Download data is not yet available.

References

Agnibha Sarkar, Dr. Ashutosh M. Kulkarni, Manish R. Khodaskar, Shubhangi Pandurang Tidake, Rahul B. Diwate : "A Predictive Model for Occurrence of Floods Using Machine Learning Techniques", Social Science Journal, vol.13, no.2, January Issue 2023.

Thegeshwar Sivamoorthy, Asif Mohammed Ansari, Dr. B. Sivakumar, V. Nallarasan : "Flood Prediction Using ML Classification Methods on Rainfall Data", International Journal for Research in Applied Science & Engineering Technology (IJRASET), DOI: https://doi.org/10.22214/ijraset.2022.41297

Kishan Kashyap M, Ajay Karthik K, ShivaKumar G.S : "Flood Prediction using Machine Learning", International Research Journal of Engineering and Technology (IRJET), Volume: 08 Issue: 07

Jeba. G. S, P. Chitra, U. M. Rajasekaran : “Time-series analysis and Flood Prediction using a Deep Learning Approach”, International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 2022. https://doi.org/10.1109/WiSPNET54241.2022.9767102

Widiasari. I. R, L. E. Nugoho, R. Efendi : “Context-based Hydrology Time Series Data for A Flood Prediction Model Using LSTM”, 5th International Conference on Information Technology, Computer, and Electrical Engineering, 16th Sept.2018. https://doi.org/10.1109/ICITACEE.2018.8576900

Babar. M, M. Rani, I. Ali : “A Deep learning-based rainfall prediction for flood management”, 17th International Conference on Emerging Technologies (ICET), 2022. https://doi.org/10.1109/ICET56601.2022.10004663

Wang. S, Wang. J : “Research on prediction model of mountain flood level in small watershed based on deep learning”, 4th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP), 2022. https://doi.org/10.1109/ICMSP55950.2022.9859047

Saiesh Naik, Anang Verma, Srushti Ashok Patil, Prof. Anil Hingmire :"Flood Prediction using Logistic Regression for Kerala State", International Journal of Engineering Research & Technology (IJERT), Volume 9, Issue 3

Jaeyeong Lee, Byunghyun Kim : "Scenario-Based Real-Time Flood Prediction with Logistic Regression", Water 2021, 13, 1191, https://doi.org/10.3390/w13091191

Muhammad Ikbal Hidayat Jati, Suroso and Purwanto Bekti Santoso : "Prediction of flood areas using the logistic regression method (case study of the provinces Banten, DKI Jakarta, and West Java)", IOP Publishing, Journal of Physics: Conference Series, DOI 10.1088/1742-6596/1367/1/012087

Naveed Ahamed, S.Asha : "Flood prediction forecasting using machine Learning Algorithms", International Journal of Scientific & Engineering Research Volume 11, Issue 12, December-2020

Mr. B. Samuel John Peter, Mr. N. Thilak Chandhra, Mr. Shaik.Afrid, Mr. N. Prasanna Kumar : "MACHINE LEARNING BASED FLOOD PREDICTION", International Journal of Research in Engineering, IT and Social Sciences, Volume 12 Issue 12, December 2022

Hussein. E, M. Ghaziasgar, C. Thron : “Regional Rainfall Prediction Using Support Vector Machine Classification of Large-Scale Precipitation Maps”, IEEE 23rd International Conference on Information Fusion (FUSION), 2020. https://doi.org/10.23919/FUSION45008.2020.9190285

Samantaraya. S, A. Sahoo, A. Agnihotri : “Prediction of Flood Discharge Using Hybrid PSO-SVM Algorithm in Barak River Basin”, MethodsX Volume 10, 2023. https://doi.org/10.1016/j.mex.2023.102060

Shada. B, N. R. Chithra, S. G. Thampi : “Hourly Flood Forecasting Using Hybrid Wavelet-SVM”, Journal of Soft Computing in Civil Engineering, 2022. https://doi.org/10.22115/SCCE.2022.317761.1383

Nadia Zehra : "Prediction Analysis of Floods Using Machine Learning Algorithms (NARX & SVM)", International Journal of Sciences: Basic and Applied Research (IJSBAR) (2020) Volume 49, No 2, pp 24-34, https://core.ac.uk/download/pdf/287366682.pdf

Shie-Yui Liong, Chandrasekaran Sivapragasam : "FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES", JAWRA JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, https://doi.org/10.1111/j.1752-1688.2002.tb01544.x

Grady. F, J. K. Tarigan, J. R. Wahidiyat, A. Prasetyo : “Classification of Flood Alert in Jakarta with Random Forest”, IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA), 2022. https://doi.org/10.1109/ICITDA55840.2022.9971411

Choi. C, J. Kim, H. Han, D. Han, H. S. Kim : “Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea”, Water, vol. 12, no. 1, p. 93, Dec. 2019. https://doi.org/10.3390/w12010093

Esfandiari. M, S. Jabari, H. Mcgrath, D. Coleman: “Flood mapping using random forest and identifying the essential conditioning factors; a case study in Fredericton, New Brunswick, Canada”, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 609–615, 2020. https://doi.org/10.5194/isprs-annals-V-3-2020-609-2020

Downloads

Published

23.02.2024

How to Cite

S, V. ., S, G. ., M, D. ., & S, K. . (2024). Rainfall Based Flood Prediction in Kerala Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 141–144. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4800

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

License

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.