Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting

• Published in: Machine learning with applications Vol. 7; p. 100204
• Main Authors: Barrera-Animas, Ari Yair, Oyedele, Lukumon O., Bilal, Muhammad, Akinosho, Taofeek Dolapo, Delgado, Juan Manuel Davila, Akanbi, Lukman Adewale
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15-03-2022; Elsevier
• Subjects: LSTM Networks; Multi-step forecast; Multivariate time-series; Rainfall prediction; Time-series data; Multivariate time-series; Multi-step forecast; Time-series data; LSTM Networks; Rainfall prediction
• ISSN: 2666-8270; 2666-8270
• DOI: 10.1016/j.mlwa.2021.100204

Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models’ performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications.
•A comparison with XGboost, AutoML and LSTM-Networks with weather time-series.•Towards the development of low-cost solutions for hourly rainfall predictions.•Bidirectional-LSTM networks can potentially be used for precipitation prediction.•Impact of hyperparameters in machine learning algorithms for prediction.