Effects of Automatic Hyperparameter Tuning on the Performance of Multi-Variate Deep Learning-Based Rainfall Nowcasting

Rainfall nowcasting has become increasingly important as we move into an era where more and more storms are occurring in many countries as a result of climate change. Developing an accurate rainfall nowcasting model could provide insights into rainfall dynamics and ultimately could prevent significant damages. In this paper, deep neural networks (DNNs) and numerical weather predictions (NWPs) are applied for rainfall and runoff forecasting in an urban catchment with a complex drainage system. DNNs are among the most accurate models for rainfall nowcasting. However, the design and training of DNNs are usually complicated. This paper combines different convolutional, long short-term memory (LSTM)-based networks and NWPs using ensemble techniques (i.e., bagging, random forest, and adaboost methods) with automatic hyperparameter tuning for multi-step rainfall nowcasting. The relative humidity, air temperature, and previous rainfall sequences are considered the inputs of the DNNs. We focus on applying two hyperparameter tuning methods (i.e., random search and tree structured Parzen estimator) to improve the performance of the proposed rainfall nowcasting models. The proposed framework was applied to the eastern drainage catchment (EDC) in Tehran city. The results illustrate that the utilization of automatic hyperparameter tuning along with multivariate DNNs, NWPs, and ensemble techniques could improve the nowcasting performance (10%-25%) compared to the traditional univariate models. Also, Adaboost is more accurate than other ensemble techniques in predicting both extreme and normal rainfall events with average RMSE of 0.765, and random forest obtain better results when predict sub normal rainfall events with overall RMSE of 0.315. The proposed framework is applicable to different climates and catchments.