Application of multimodal deep learning using radar and water level data for water level prediction

Abstract

In general, water level prediction models using deep learning techniques have been developed using time-series water level observation data from upstream water level stations and target water level stations even though many of physical data are necessary to predict water level. The changes of the water level are greatly affected by rainfall in the basin, therefore rainfall information is needed to more accurately predict the water level. In particular, radar data has the advantage of being able to directly acquire the amount of rainfall occurring within a watershed. This study aims to develop the multimodal deep learning model to predict the water level using 2D grid radar rainfall data and 1D time-series water level observation data. This study proposed two multimodal deep learning models which have different structures. Both multimodal deep learning models predict the water level by simultaneously using the observed water level data up to the present time and the radar rainfall data that affects the water level in the future. The first proposed model consists of a deep learning network that links 2D Average Pooling (AvgPool2D), which compresses 2D radar data to 1D data, and Long Short-Term Memory (LSTM), which predicts 1D time series water level data. The second proposed model consists of a deep learning network that predicts water levels by linking Conv2DLSTM and LSTM, which can reflect the characteristics of 2D radar data without deformation.  The two proposed multimodal deep learning models were learned and evaluated in the upper basin of Hantan River. In addition, it was compared with the results of single-modal LSTM using only water level data. There are three water level stations in the study area, and the objective was to predict the water level of the downstream station up to 180 minutes in advance. For learning and verification of the deep learning model, 10-minute water level and radar rainfall data were collected from May 2019 to October 2021. For the radar data used as input, the grid data included in the target watershed were extracted and used among composite radar data with a resolution of 1 km operating by Ministry of Environment. As a result of evaluating each learned deep learning model, two multimodal models had higher prediction accuracy than the single-modal using only water level data. In particular, second proposed model (Conv2dLSTM+LSTM) had better predictive performance than first proposed model (AvgPool2D+LSTM) at the time of the sudden rise in water level due to rainfall.AcknowledgmentsResearch for this paper was carried out under the KICT Research Program (project no. 202200175-001, Development of future-leading technologies solving water crisis against to water disasters affected by climate change) funded by the Ministry of Science and ICT.