Flood prediction and management
Abstract
Floods are one of the most devastating forces of nature, that destroy thousands of lives every year. Not only does the economy of a country suffer because of such a disaster, but the loss of agriculture and people is physically and mentally exhausting for a country. Especially, in a country like India where floods are frequent, and the prevention department lacks, it becomes crucial to early detect these floods, and inform the local authorities to safeguard the lives of thousands of people.
Through this paper, we aim to work on a comprehensive flood prediction system that utilizes machine learning algorithms to enhance the efficiency and accuracy of a flood prediction and management system. In this study, we have used the dataset with 142193 entries and to work with such large data we have used multiple algorithms. These machine learning algorithms have made it easy to analyze or to work with large datasets. We used multiple algorithms that have worked well with this dataset but some of them have performed better than others. Out of all algorithms, XGBoost has performed best. Along with XGBoost algorithms like CatBoost and Random Forest have also performed well as they all have accuracies of more than 90%.
Our target is accurate and early prediction of floods in an area, and then to inform the required local authorities about the forecast. So, that necessary action can be taken, and the flood-prone area can be evacuated in an organized manner.
Downloads
References
Mosavi, A., Ozturk, P., Chau, K.: Flood Prediction Using Machine Learning Models: Literature Review. Water, 10, pp. 1-41 (2018).
Felix, A., Sasipraba, T.: Flood Detection Using Gradient Boost Machine Learning Approach. In: International Conference on Computational Intelligence and Knowledge Economy, pp. 779-783 (2019).
Ying, B., Sayed, A.: Diffusion gradient boosting for networked learning. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2512-2516 (2017).
Adnan, R.; Zain, Z.M.; Ruslan, F.A. 5 hours flood prediction modeling using improved NNARX structure: Case study Kuala Lumpur. In Proceedings of the 2014 IEEE 4th International Conference on System Engineering and Technology (ICSET), Bandung, Indonesia, 24–25 November 2014; IEEE: New York, NY, USA, 2014.
Mosavi, A.; Ozturk, P.; Chau, K.-W. Flood prediction using machine learning models: Literature review. Water 2018, 10, 1536. [CrossRef].
Chen, C.; Jiang, J.; Liao, Z.; Zhou, Y.; Wang, H.; Pei, Q. A short-term flood prediction based on spatial deep learning network: A case study for Xi County, China. J. Hydrol. 2022, 607, 127535. [CrossRef].
Maspo, N.-A.; Bin Harun, A.N.; Goto, M.; Cheros, F.; Haron, N.A.; Nawi, M.N.M. Evaluation of Machine Learning approach in flood prediction scenarios and its input parameters: A systematic review. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2020.
Puttinaovarat, S.; Horkaew, P. Flood forecasting system based on integrated big and crowdsource data by using machine learning techniques. IEEE Access 2020, 8, 5885–5905. [CrossRef].
Ghorpade, P.; Gadge, A.; Lende, A.; Chordiya, H.; Gosavi, G.; Mishra, A.; Hooli, B.; Ingle, Y.S.; Shaikh, N. Flood forecasting using machine learning: A review. In Proceedings of the 2021 8th International Conference on Smart Computing and Communications (ICSCC), Kerala, India, 1–3 July 2021; IEEE: New York, NY, USA, 2021.
Furquim, G.; Pessin, G.; Faiçal, B.S.; Mendiondo, E.M.; Ueyama, J. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: A case study involving a real wireless sensor network deployment in brazil. Neural Comput. Appl. 2016, 27, 1129–1141. [CrossRef].
Adnan, M.S.G.; Siam, Z.S.; Kabir, I.; Kabir, Z.; Ahmed, M.R.; Hassan, Q.K.; Rahman, R.M.; Dewan, A. A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction. J. Environ. Manag. 2023, 326, 116813. [CrossRef] [PubMed].
Talukdar, S.; Ghose, B.; Shahfahad; Salam, R.; Mahato, S.; Pham, Q.B.; Linh, N.T.T.; Costache, R.; Avand, M. Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms. Stoch. Environ. Res. Risk Assess. 2020,34, 2277–2300. [CrossRef].
Gauhar, N.; Das, S.; Moury, K.S. Prediction of flood in Bangladesh using K-nearest neighbors’ algorithm. In Proceedings of then. 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 5–7 January 2021; IEEE: New York, NY, USA, 2021.
Hamidul Haque, M.; Sadia, M.; Mustaq, M. Development of Flood Forecasting System for Someshwari-Kangsa Sub-watershed of Bangladesh-India Using Different Machine Learning Techniques. EGU General Assembly Conference Abstracts; EGU: Virtual, 2021. Available online: https://ui.adsabs.harvard.edu/abs/2021EGUGA..2315294H/abstract (accessed on 20 October 2023)
Hossain, I.; Rasel, H.M.; Alam Imteaz, M.; Mekanik, F. Long-term seasonal rainfall forecasting using linear and non-linear modelling approaches: A case study for Western Australia. Meteorol. Atmos. Phys. 2020, 132, 131–141. [CrossRef]
Aswad, F.M.; Kareem, A.N.; Khudhur, A.M.; Khalaf, B.A.; Mostafa, S.A. Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction. J. Intell. Syst. 2021, 31, 1–14. [CrossRef]
Ighile, E.H.; Shirakawa, H.; Tanikawa, H. Application of GIS and machine learning to predict flood areas in Nigeria. Sustainability 2022, 14, 5039. [CrossRef]
Kunverji, K.; Shah, K.; Shah, N. A flood prediction system developed using various machine learning algorithms. In Proceedings of the 4th International Conference on Advances in Science & Technology (ICAST2021), Mumbai, India, 7 May 2021.
]Sarasa-Cabezuelo, A. Prediction of rainfall in Australia using machine learning. Information 2022, 13, 163. [CrossRef]
Liyew, C.M.; Melese, H.A. Machine learning techniques to predict daily rainfall amount. J. Big Data 2021, 8, 153. [CrossRef]
Copyright (c) 2024 ITEGAM-JETIA
This work is licensed under a Creative Commons Attribution 4.0 International License.
