Evaluation of the Performance of Categorical Boosting Algorithm for Flood Prediction in Osun River Basin | ||||
| Journal of Computing and Communication | ||||
| Article 3, Volume 3, Issue 2 - Serial Number 1, July 2024, Page 23-30 PDF (719.44 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2024.380114 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Oluwatosin I Ogundolie   1; Stephen Olatunde Olabiyisi 1; Rafiu Adesina Ganiyu1; Yetomiwa Sinat Jeremiah 1; Frank Abimbola Ogundolie 2
| ||||
| 1Department of Computer Science, Ladoke Akintola University, Ogbomoso, Nigeria | ||||
| 2Department of Biotechnology, Faculty of Computing and Applied Sciences, Baze University Abuja, Nigeria | ||||
| Abstract | ||||
| Flooding is the third biggest disaster in the world according to the World Meteorological Organization. Several methods like numerical models, physical models, and Machine Learning (ML) models have been engaged in flood prediction to minimize the impact of flooding. Despite the improvements experienced in the use of some ML methods, there are still drawbacks due to accuracy. Hence, this study evaluated Categorical Boosting Algorithm (CatBoost) for flood prediction based on some evaluation metrics. Relevant flood-predictive factors were identified from the Osun River basin. The data was split into 70% for the training and 30% for the testing of the algorithm. The flood dataset was imported into the CatBoost Algorithm using Python programming language with the default parameters of the algorithm. The algorithm was evaluated using accuracy, precision, sensitivity, and multiclass loss function. The results showed that the accuracy, precision, and sensitivity of the CatBoost Algorithm were 92.48%, 63.82%, and 85.86% respectively. The result of the multiclass loss function during validation was 0.165874, which was significantly lower than the result during training, which was 0.925104. This indicates that the algorithm is overfitting the training data and is not generalizing well to new data. This can be a prospect for further study. | ||||
| Keywords | ||||
| Categorical Boosting; Algorithm; Flooding; Machine learning; Evaluation | ||||
| References | ||||
|
[1] Deng, G., Chen, H., & Wang, S. Risk assessment and prediction of rainstorm and flood disaster based on henan province, China. Mathematical Problems in Engineering, (2022). [2] Dorogush, A. V., Ershov, V., and Gulin, A. CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363, 2018. [3] Gebrehiwot, A., Hashemi-Beni, L., Thompson, G., Kordjamshidi, P., and Langan, T. E.. Deep convolutional neural network for flood extent mapping using unmanned aerial vehicles data. Sensors, (2019), 19(7): 1486. [4] Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T. and Yang, H.. Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters, (2019), 14(12), 124007. [5] Ighile, E. H., Shirakawa, H., and Tanikawa, H.. Application of GIS and Machine Learning to Predict Flood Areas in Nigeria. Sustainability, (2022), 14(9), 5039. [6] Ma, M., Zhao, G., He, B., Li, Q., Dong, H., Wang, S., and Wang, Z.. XGBoost-based method for flash flood risk assessment. Journal of Hydrology, (2021), 598, 126382. [7] Michael, E. B. and Patience O. E.. Flood prediction in Nigeria using artificial neural network. American Journal of Engineering Research, (2018), 7(9): 15-21. [8] Mosavi, A., Ozturk, P., and Chau, K. W.. Flood prediction using machine learning models: Literature review. Water, (2018), 10(11): 1536. [9] Muñoz, P., Orellana-Alvear, J., Willems, P., and Célleri, R.. Flash-flood forecasting in an Andean mountain catchment—Development of a step-wise methodology based on the random forest algorithm. Water, (2018), 10(11): 1519. [10] Obeta, M. C.. Institutional Approach to Flood Disaster Management in Nigeria: Need for a Preparedness Plan. British Journal of Applied Science & Technology, (2014), 4(33): 4575-4590. [11] Peretz T.. Mastering The New Generation of Gradient Boosting. https://towardsdatascience.com/https-medium-com-talperetz24-mastering-the-new-generation-of-gradient-boosting-db04062a7ea2 . (2018) Accessed on 29th December 2021 [12] Shaharkar, Y., Sonar, A., Sonar, C. and Pawar D. Flood Damage Estimation using Machine Learning in GIS. International Research Journal of Engineering and Technology. (2020), 7(6) :5756 – 5760 [13] Syifa, M., Park, S., Achmad, A., Lee, C., and Eom, J.. Flood Mapping Using Remote Sensing Imagery and Artificial Intelligence Techniques: A Case Study in Brumadinho, Brazil. Journal of Coastal Research, 2019, 197-204. [14] Umar, N., and Gray, A.. Flooding in Nigeria: a review of its occurrence and impacts and approaches to modelling flood data. International Journal of Environmental Studies, 2022, 1-22. [15] Yin, J., and Li, N.. Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping. Ore Geology Reviews, 2022(145), 104916 | ||||
|
Statistics Article View: 149 PDF Download: 160 |
||||
