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-min 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.
Highlights
Quantitative precipitation forecasts (QPFs) with high spatial–temporal resolution and accuracy with several hours of lead time can provide rainfall forecasts, which are valuable in hydrological predictions of urban flood practices
The goals of the models are to reproduce rainfall forecasts with a 180 min lead time based on observed rainfall and 25 data points around rain gauges from the MAPLE system
The above results prove that the accuracy of the long short-term memory (LSTM)-corrected rainfall forecasts is
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Precipitation forecasts are a primary driver of flood forecasting, water management, and hydrologic modeling studies in urban areas. Quantitative precipitation forecasts (QPFs) with high spatial–temporal resolution and accuracy with several hours of lead time can provide rainfall forecasts, which are valuable in hydrological predictions of urban flood practices. The success of hydrological predictions is highly dependent on the effectiveness of forecasted rainfall, which is still a challenge for the forecasting systems
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