Flood Prediction using MLP, CATBOOST and Extra-Tree Classifier
Article Sidebar
Main Article Content
Abstract
Flooding can be one of the many devastating natural catastrophes, resulting in the annihilation of life and damaging property. Additionally, it can harm farmland and kill growing crops and trees. Nowadays, rivers and lakes are being destroyed, and the natural water reservoirs are converted into development sites and buildings. Due to this, even just a bit of rain can cause a flood. To minimize the number of fatalities, property losses, and other flood-related issues, an early flood forecast is necessary. Therefore, machine learning methods can be used for the prediction of floods.
To forecast the frequency of floods brought on by rainfall, a forecasting system is built using rainfall data. The dataset is trained using various techniques like the MLP classifier, the CatBoost classifier, and the Extra-Tree classifier to predict the occurrence of floods. Finally, the three models' performances are compared and the best model for flood prediction is presented. The MLP, Extra-Tree, and CatBoost models achieved accuracy of 94.5%, 97.9%, and 98.34%, respectively, and it is observed that CatBoost performed well with high accuracy to predict the occurrence of floods.
Article Details
References
Jeerana Noymanee, Thanaruk Theeramunkong, “Flood Forecasting with Machine Learning Technique on Hydrological Modeling”, Procedia Computer Science, Volume 156, 2019, pp. 377-386.
Ghazaly, N. M. . (2020). Secure Internet of Things Environment Based Blockchain Analysis. Research Journal of Computer Systems and Engineering, 1(2), 26:30. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/8
Z. K. Lawal, H. Yassin and R. Y. Zakari, "Flood Prediction Using Machine Learning Models: A Case Study of Kebbi State Nigeria," 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Brisbane, Australia, 2021,pp.1-6,doi: 10.1109/CSDE53843.2021.9718497.
A. B. Ranit and P. V. Durge, "Flood Forecasting by Using Machine Learning," 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2019,pp.166-169,doi: 10.1109/ICCES45898.2019.9002579.
C. Kinage, A. Kalgutkar, A. Parab, S. Mandora and S. Sahu, "Performance Evaluation of Different Machine Learning Based Algorithms for Flood Prediction and Model for Real Time Flood Prediction," 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India, 2019, pp. 1-7, doi: 10.1109/ICCUBEA47591.2019.9128379.
Dr. Bhushan Bandre. (2013). Design and Analysis of Low Power Energy Efficient Braun Multiplier. International Journal of New Practices in Management and Engineering, 2(01), 08 - 16. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/12
Asif Syeed, Miah & Farzana, Maisha & Namir, Ishadie & Ishrar, Ipshita & Nushra, Meherin & Rahman, Tanvir, “Flood Prediction using Machine Learning models ”, 2022, doi:10.48550/arXiv.2208.01234.
Vinothini A, Kruthiga L, Monisha U, “Prediction of Flash Flood Using Rainfall by MLP Classifier,” International Journal Of recent Technology and Engineering (IJRTE), ISSN:2277-3878,Volume-9 Issue-1, May 2020.
Mohammed Khalf, Haya Alaskar, Abir Jaafar Hussian et al, “IOT- Enabled Flood Severity Via Ensemble Machine Learning Models,” Institute of Electrical and Electronics and Engineering(IEEE), Digital Object Identifier 10.1109/ACCESS.2020.2986090.
J. Akshaya and P. L. K. Priyadarshini, "A Hybrid Machine Learning Approach for Classifying Aerial Images of Flood-Hit Areas," 2019 International Conference on Computational Intelligence in Data Science(ICCIDS), 2019,pp.1-5,Doi: 10.1109/ICCIDS.2019.8862138.
J. M. A. Opella and A. A. Hernandez, "Developing a Flood Risk Assessment Using Support Vector Machine and Convolutional Neural Network: A Conceptual Framework," 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA), 2019, pp. 260-265, Doi: 10.1109/CSPA.2019.8695980.
A. B. Ranit and P. V. Durge, "Different Techniques of Flood Forecasting and Their Applications," 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), 2018, pp. 1-3, Doi: 10.1109/RICE.2018.8509058.
Pérez, C., Pérez, L., González, A., Gonzalez, L., & Ólafur, S. Personalized Learning Paths in Engineering Education: A Machine Learning Perspective. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/107
Halit Enes Aydin & Muzaffer Can Iban, 2023. "Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2957-2991, December-2022.
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 and Engineering Technology, Volume 10 Issue 4 Apr 2022.
Maria Gonzalez, Machine Learning for Anomaly Detection in Network Security , Machine Learning Applications Conference Proceedings, Vol 1 2021.
Dr. V.V. Ramalingam, Rohan Mishra, Sagar Parashari, “Prediction of Flood by Rainfall using MLP Classifier of Neural Network Model”, International Journal of Advanced Science and Technology, 2020, Volume 29 Issue 06, pp.2804 - 2812
P. Ghorpade et al., "Flood Forecasting Using Machine Learning: A Review," 2021 8th International Conference on Smart Computing and Communications (ICSCC), Kochi, Kerala, India, 2021, pp. 32-36 , doi: 10.1109/ICSCC51209.2021.9528099.
Ainaa Hanis Zuhairi,Fitri Yakub,Sheikh Ahmad Zaki,Mohamed Sukri Mat Ali,"Review of flood prediction hybrid machine learning models using datasets",The 9th AUN/SEED-Net Regional Conference on Natural Disaster, 2022, doi:10.1088/1755-1315/1091/1/012040
Danso-Amoako et al. “Predicting dam failure risk for sustainable flood retention basins:” A generic case study for the wider greater manchester area. Comput. Environ. Urban Syst. 2012, 36, 423–433.
Kumar , D. A. ., & Das, S. K. . (2023). Machine Learning Approach for Malware Detection and Classification Using Malware Analysis Framework. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 330–338. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2543
Thakre, B., Thakre, R., Timande, S., & Sarangpure, V. (2021). An Efficient Data Mining Based Automated Learning Model to Predict Heart Diseases. Machine Learning Applications in Engineering Education and Management, 1(2), 27–33. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/17
F. R. G. Cruz, M. G. Binag, M. R. G. Ga and F. A. A. Uy, "Flood Prediction Using Multi-Layer Artificial Neural Network in Monitoring System with Rain Gauge, Water Level, Soil Moisture Sensors," TENCON 2018 - 2018 IEEE Region 10 Conference, 2018, pp. 2499-2503, DOI: 10.1109/TENCON.2018.8650387.
G. Kaur and A. Bala, "An Efficient Automated Hybrid Algorithm to Predict Floods in Cloud Environment," 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), 2019, pp. 1-4, Doi: 10.1109/CCECE.2019.8861897.
Amir Mosavi , Pinar Ozturk and Kwok-wing Chau , “Flood Prediction Using Machine Learning Models,” Literature Review by Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU), Trondheim, NO-7491, Norway.
F. A. Ruslan, K. Haron, A. M. Samad and R. Adnan, "Multiple Input Single Output (MISO) ARX and ARMAX model of flood prediction system: Case study Pahang," 2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA), 2017, pp.179-184,Doi:10.1109/CSPA.2017.8064947