A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities
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- Nathan S. Debortoli & Pedro Ivo M. Camarinha & José A. Marengo & Regina R. Rodrigues, 2017. "An index of Brazil’s vulnerability to expected increases in natural flash flooding and landslide disasters in the context of climate change," 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. 86(2), pages 557-582, March.
- Ilona M. McNeill & Patrick D. Dunlop & Jonathan B. Heath & Timothy C. Skinner & David L. Morrison, 2013. "Expecting the Unexpected: Predicting Physiological and Psychological Wildfire Preparedness from Perceived Risk, Responsibility, and Obstacles," Risk Analysis, John Wiley & Sons, vol. 33(10), pages 1829-1843, October.
- Chris Funk & Molly Brown, 2009. "Declining global per capita agricultural production and warming oceans threaten food security," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 1(3), pages 271-289, September.
- J. Park & T. P. Seager & P. S. C. Rao & M. Convertino & I. Linkov, 2013. "Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems," Risk Analysis, John Wiley & Sons, vol. 33(3), pages 356-367, March.
- Vijendra Kumar & Hazi Md. Azamathulla & Kul Vaibhav Sharma & Darshan J. Mehta & Kiran Tota Maharaj, 2023. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management," Sustainability, MDPI, vol. 15(13), pages 1-33, July.
- Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).
- M. A. Ghorbani & R. Khatibi & V. Karimi & Zaher Mundher Yaseen & M. Zounemat-Kermani, 2018. "Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4201-4215, October.
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- Fatemeh Ghobadi & Amir Saman Tayerani Charmchi & Doosun Kang, 2023. "Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction," Sustainability, MDPI, vol. 15(22), pages 1-32, November.
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Keywords
rainfall forecasting; machine learning; Catboost; Lasso; Ridge; LGBM; XGBoost;All these keywords.
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