A contact model based on the SQP algorithm and engineering application

Establishing a model predicting river flow can effectively reduce huge losses caused by floods. This paper proposes a multi-step time series forecasting model based on multiple input and multiple output strategies, and this model is applied to the flood forecasting process of a river basin in Shanxi, which effectively improves the engineering application value of the flood forecasting model based on deep learning. The experimental results show that after considering the seasonal characteristics of the river channel and screening the influencing factors, a simple neural network model can accurately predict the peak value, the peak time and flood trends. On this basis, we proposed the MSBP (Multi-step Back Propagation) model, which can accurately predict the flow trend of the river basin 20 h in advance, and the NSE (Nash Efficiency) is 0.89. The MSBP model can improve the reliability of flood forecasting and increase the internal interpretability of the model, which is of great significance for effectively improving the effect of flood forecasting.

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Energy Consumption of the VAC System for Subway Stations: A Model Based on Theoretical Analysis and its Engineering Application

E3S Web of Conferences ◽

10.1051/e3sconf/201911106042 ◽

2019 ◽

Vol 111 ◽

pp. 06042

Author(s):

Ziyi Su ◽

Xiaofeng Li

Keyword(s):

Energy Consumption ◽

Theoretical Analysis ◽

Energy Saving ◽

Thermal Environment ◽

Engineering Application ◽

Environmental Parameters ◽

Energy Model ◽

Simulation Software ◽

Model Based ◽

Subway Stations

With the continuous expansion of the subway scale, the amount of energy consumed by subway stations has become a concern, among which the energy consumption of ventilation and air-conditioning (VAC) system takes a large proportion. There are many former studies concentrated on this topic. However, the problems of previous studies are obvious, such as time-consuming and unclear practical meaning of the energy model. This study has proposed a model based on theoretical analysis and focused on the energy model of the VAC system for underground subway stations. The energy model consists of the model for the cooling load and the energy efficient ratio. In this model, basic station information and environmental parameters should be tested and inputted. Then with the help of simple simulation tools, Subway Thermal Environment Simulation Software (STESS), the energy consumption of the station can be fast estimated by the model proposed in this paper. This model can be applied to determine the energy-saving potential of changing operation pattern and adopting energy-saving technologies, which gives guidance to the energy-saving retrofit of the station.

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Deep Text Information Type Detection Model Based on Statistical Learning in Engineering Application

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/769/4/042070 ◽

2021 ◽

Vol 769 (4) ◽

pp. 042070

Author(s):

Chengfu Wei

Keyword(s):

Statistical Learning ◽

Engineering Application ◽

Detection Model ◽

Model Based ◽

Text Information ◽

Information Type

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