A neural network encoder-decoder for time series prediction: Application on 137Cs particulate concentrations in nuclearized rivers

Abstract

Monitoring the impact of human activities on the environment is a major challenge as many pollutants can be found in the different ecosystems. This is the case of the caesium-137 that has been present in the environment for many decades as a result of atmospheric tests, accidents such as Chernobyl and release from nuclear industries. With the recent advance in data-driven models, this study evaluate the relevance of a deep learning tool for reconstructing caesium-137 chronics particulate concentration in rivers. An encoder-decoder neural network, “Hierarchical Attention-Based Recurrent Highway Networks”(HRHN), is proposed notably for its ability to extract the most relevant temporal and spatial information from the databases. Three monitoring stations were studied, one on the Rhône River and two on the Loire River, all of them downstream nuclear industries in these catchments affected by the global fallout and the accident of Chernobyl. The objective is to predict the future concentration from a set of variables providing past information on water discharge, washout flux and industrial radioactive releases. Once optimised, the model generates first results in agreement with the real concentration curves by correctly following the main trends, with a NSE of 0.89, 0.53 and 0.35 respectively for the Rhone station and the two stations on the Loire. The main reason of inaccuracies is due to the quantity of data available. The originality of this model is its capacity to make predictions on different catchment areas. In fact the training was conducted on the Rhône station as the range of the concentration was higher (from 265.4 to 2700.0 Bq/kg) and the testing on the two Loire station. Another encoder-decoder model DA-RNN (Dual-Stage Attention-Based Recurrent Neural Network) was also evaluate in order to compare the performance of an alternative architecture, without convolution layer. The conclusion is that HRHN remains more powerful in the predictions on the 3 systems. With these first interesting results for HRHN, further investigations should be taken into account for other pollutants than caesium-137 to better understand the robustness of the model.