Improving Radar-Based Rainfall Forecasts by Long Short-Term Memory Network in Urban Basins

Abstract

Radar-based rainfall forecasts are widely used extrapolation algorithms that are popular in systems of precipitation for predicting up to six hours in lead time. Nevertheless, the reliability of rainfall forecasts gradually declines for heavy rain events with lead time due to the lack of predictability. Recently, data-driven approaches were commonly implemented in hydrological problems. In this research, the data-driven models were developed based on the data obtained from a radar forecasting system named McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE) and ground rain gauges. The data included thirteen urban stations in the five metropolitan cities located in South Korea. The twenty-five data points of MAPLE surrounding each rain station were utilized as the model input, and the observed rainfall at the corresponding gauges were used as the model output. The results showed superior capabilities of long short-term memory (LSTM) network in improving 180-minute rainfall forecasts at the stations based on a comparison of five different data-driven models, including multiple linear regression (MLR), multivariate adaptive regression splines (MARS), multi-layer perceptron (MLP), basic recurrent neural network (RNN), and LSTM. Although the model still produced an underestimation of extreme rainfall values at some examined stations, this study proved that the LSTM could provide reliable performance. This model can be an optional method for improving rainfall forecasts at the stations for urban basins.

Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

“…As suggested in the package documentation and literature [59], [60], the parameter for an RF should be sufficiently large. The parameters and were tuned in the ranges of [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] and [1000-2000], respectively, with the grid search method.…”

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

“…The authors showed that their approach outperforms the EMOS (Ensemble Model Output Statistics) method. Using the data obtained from a radar forecasting system named McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE) (Germann and Zawadzki, 2002) and ground rain gauges, Nguyen et al (2021) employed LSTMs. They showed that LSTMs significantly improved MAPLE forecasts for South Korea.…”

Introducing Temporal Correlation in Rainfall and Wind Prediction From Underwater Noise

“…Similarly, for rainfall intensity, both statistical analysis of time series and ML models have been proposed to forecast precipitation over successive days [28], [29], [30]; successive hours [31], [32], [33], [34], [35]; or sequences of shorter intervals [36]. For short-term forecasting (i.e., limited to a few hours ahead), the input variables generally consist of the rainfall intensity, and sometimes other meteorological quantities, recorded over a period going from the last 45 min to the last 3 h [31], [32], [33], [34], [35], [36]. As for wind, the most recent contributions [29], [30], [34], [35] focus on the LSTM network.…”

“…For short-term forecasting (i.e., limited to a few hours ahead), the input variables generally consist of the rainfall intensity, and sometimes other meteorological quantities, recorded over a period going from the last 45 min to the last 3 h [31], [32], [33], [34], [35], [36]. As for wind, the most recent contributions [29], [30], [34], [35] focus on the LSTM network. According to the results reported in previous papers, Barrera-Animas et al [35] state that forecast models based on LSTM networks "outperform other models in the task of forecasting rainfall on an hourly, daily, and monthly basis.…”