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Songtao Huang, Jun Shen, Qingquan Lv, Qingguo Zhou and Binbin Yong
Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditi...
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Guowei Hua, Shijie Wang, Meng Xiao and Shaohua Hu
Dam safety is considerably affected by seepage, and uplift pressure is a key indicator of dam seepage. Thus, making accurate predictions of uplift pressure trends can improve dam hazard forecasting. In this study, a convolutional neural network, (CNN)-ga...
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Biagio Saya and Carla Faraci
In the hydraulic construction field, approximated formulations have been widely used for calculating tank volumes. Identifying the proper water reservoir volumes is of crucial importance in order to not only satisfy water demand but also to avoid unneces...
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Edgar Acuna, Roxana Aparicio and Velcy Palomino
In this paper we investigate the effect of two preprocessing techniques, data imputation and smoothing, in the prediction of blood glucose level in type 1 diabetes patients, using a novel deep learning model called Transformer. We train three models: XGB...
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Li He, Shasha Ji, Kunlun Xin, Zewei Chen, Lei Chen, Jun Nan and Chenxi Song
Hydraulic monitoring data is critical for optimizing drainage system design and predicting system performance, particularly in the establishment of data-driven hydraulic models. However, anomalies in monitoring data, caused by sensor failures and network...
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