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

Abstract

Climate change has increased the importance of rainfall forecasting, because it has changed the duration and frequency of floods and increased their magnitude Ashrafi, Khoie, et al., 2022). As a result of inundation, landslides, and debris flows in urban areas, heavy rainfall often leads to fatalities and significant financial damages (Seo et al., 2014). Furthermore, overflow resulting from heavy rainfall is a primary contributor to the transport of water pollutants (Lintern et al., 2018). Various preventive measures have been developed, including flood warning systems, to alleviate damages caused by rainfall and enable urban flood management infrastructures to operate more efficiently. Therefore, accurate and reliable rainfall forecasting is essential in urban areas (Poornima & Pushpalatha, 2019). Reliable rainfall nowcasting is a challenging and complex undertaking since rainfall features are influenced by various variables, such as pressure, temperature, relative humidity, and wind speed (Govindaraju, 2000). Due to the difficulty in obtaining meteorological data and the complexity of estimating relationships among variables, physical models are not usually suitable for rainfall nowcasting. An effective alternative is to use empirical models to determine the relationship between inputs like temperature and relative humidity and outputs like rainfall (Nasseri et al., 2008). To ensure optimal performance for urban drainage infrastructures, rainfall nowcasting frameworks should be able to make accurate forecasts with different lead times. Researches on rainfall forecasting and its influencing factors have been conducted for decades, and many approaches have been proposed. There are various traditional forecasting models in the literature, which have been developed using classic time series algorithms such as linear regression, autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural networks (ANNs), numerical weather predictions (NWPs), and Fuzzy logic algorithm (

Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning

“…For example, the parameters of the ConvLSTM network are the number of hidden units, the filter size, mini-batch size, learning rate, and the dropout probability. In general, applying parameters using the rule of thumb or previous research results in the best results, but we have tried to find the best parameter to achieve optimal performance, using automatic parameter tuning for DNNs [ 47 ]. There are different automatic hyperparameter tuning algorithms including search, random search, and Bayesian optimization.…”

Temporal and Spatial Satellite Data Augmentation for Deep Learning-Based Rainfall Nowcasting

“…Table 1 presents the hyperparameters utilized in the study. The utilization of the Adam (adaptive moment estimation) optimizer, an algorithm based on gradient descent that integrates the advantageous characteristics of AdaGrad and RMSProp algorithms, has proven to be effective in rainfall nowcasting and the gradual convergence to the minimum of the loss function (Amini et al, 2023). Additionally, it encompasses outliers (Kim and Han, 2020).…”

“…The Rectified Linear Unit (ReLU) activation function has been observed to mitigate the issue of vanishing gradient. Activation functions such as ReLU and its derivatives, including Leaky ReLU, are frequently employed in the field of deep learning (Moishin et al, 2021;Kumar, 2021;Amini et al, 2023). ReLU provides fixed derivatives in positive values that allow the training process to continue even when input values are extreme (Kim & Han, 2020).…”