Matteo Salis , Abdourrahmane M. Atto , Stefano Ferraris , Rosa Meo
{"title":"纯外生预测的时间分布深度学习模型:使用天气图像时间序列进行地下水位预测的应用","authors":"Matteo Salis , Abdourrahmane M. Atto , Stefano Ferraris , Rosa Meo","doi":"10.1016/j.envsoft.2025.106568","DOIUrl":null,"url":null,"abstract":"<div><div>Deep Learning (DL) models have revealed to be very effective in hydrology, especially in handling spatially distributed data (e.g. raster data). We have proposed two different DL models to predict the water table depth in the Grana-Maira catchment (Piedmont, IT) using only exogenous weather image time series. Both the models are made of a first Time Distributed Convolutional Neural Network (TDC) which encodes the images into hidden vectors. The first model, TDC-LSTM uses then a Sequential Module based on an LSTM layer to learn temporal relations and output the predictions. The second model, TDC-UnPWaveNet uses instead a new version of the WaveNet architecture, adapted for handling output of different length and completely shifted in the future to the input. Both models have shown remarkable results focusing on different learnable information: TDC-LSTM has focused more on bias while the TDC-UnPWaveNet more on the temporal dynamics maximizing correlation <span><math><mi>ρ</mi></math></span>, achieving mean BIAS (and standard deviation) −0.18(0.05), −0.25(0.19) and <span><math><mi>ρ</mi></math></span> 0.93(0.03), 0.96(0.01) respectively over all the sensors.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106568"},"PeriodicalIF":4.8000,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Predictions Using Weather Image Time Series\",\"authors\":\"Matteo Salis , Abdourrahmane M. Atto , Stefano Ferraris , Rosa Meo\",\"doi\":\"10.1016/j.envsoft.2025.106568\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Deep Learning (DL) models have revealed to be very effective in hydrology, especially in handling spatially distributed data (e.g. raster data). We have proposed two different DL models to predict the water table depth in the Grana-Maira catchment (Piedmont, IT) using only exogenous weather image time series. Both the models are made of a first Time Distributed Convolutional Neural Network (TDC) which encodes the images into hidden vectors. The first model, TDC-LSTM uses then a Sequential Module based on an LSTM layer to learn temporal relations and output the predictions. The second model, TDC-UnPWaveNet uses instead a new version of the WaveNet architecture, adapted for handling output of different length and completely shifted in the future to the input. Both models have shown remarkable results focusing on different learnable information: TDC-LSTM has focused more on bias while the TDC-UnPWaveNet more on the temporal dynamics maximizing correlation <span><math><mi>ρ</mi></math></span>, achieving mean BIAS (and standard deviation) −0.18(0.05), −0.25(0.19) and <span><math><mi>ρ</mi></math></span> 0.93(0.03), 0.96(0.01) respectively over all the sensors.</div></div>\",\"PeriodicalId\":310,\"journal\":{\"name\":\"Environmental Modelling & Software\",\"volume\":\"193 \",\"pages\":\"Article 106568\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Modelling & Software\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S136481522500252X\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S136481522500252X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Predictions Using Weather Image Time Series
Deep Learning (DL) models have revealed to be very effective in hydrology, especially in handling spatially distributed data (e.g. raster data). We have proposed two different DL models to predict the water table depth in the Grana-Maira catchment (Piedmont, IT) using only exogenous weather image time series. Both the models are made of a first Time Distributed Convolutional Neural Network (TDC) which encodes the images into hidden vectors. The first model, TDC-LSTM uses then a Sequential Module based on an LSTM layer to learn temporal relations and output the predictions. The second model, TDC-UnPWaveNet uses instead a new version of the WaveNet architecture, adapted for handling output of different length and completely shifted in the future to the input. Both models have shown remarkable results focusing on different learnable information: TDC-LSTM has focused more on bias while the TDC-UnPWaveNet more on the temporal dynamics maximizing correlation , achieving mean BIAS (and standard deviation) −0.18(0.05), −0.25(0.19) and 0.93(0.03), 0.96(0.01) respectively over all the sensors.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.