Estimation of Surface Soil Moisture Using a Structural Equation Model and an Artificial Neural Network (SEM-ANN) Combined Method

2021 ◽  
Author(s):  
sinan wang ◽  
Ruiping Li ◽  
yingjie wu ◽  
shuixia zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Surface Soil ◽  
Equation Model ◽  
Surface Soil Moisture ◽  
Combined Method ◽  
Artificial Neural

scholarly journals The Acceptance of Independent Autonomous Vehicles and Cooperative Vehicle-Highway Autonomous Vehicles

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 346
Author(s):  
Shuang Zhang ◽  
Peng Jing ◽  
Gang Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Equation Model ◽  
Autonomous Vehicles ◽  
Structural Equation ◽  
Autonomous Driving ◽  
Equation Model ◽  
Use Of Technology ◽  
Artificial Neural ◽  
Input Variables

The public’s acceptance of independent autonomous vehicles and cooperative vehicle-highway autonomous vehicles is studied by combining the structural equation model and an artificial neural network. The structural equation model’s output variables are used as the input variables of the artificial neural network, which compensates for the linear problem of the structural equation model and ensures the accuracy of the input variables of the artificial neural network. In order to summarize the influencing factors of autonomous vehicles acceptance, the Unified Theory of Acceptance and Use of Technology model was expanded by adding two variables: risk expectation and consumer innovation. The results show that social influence is the strongest predictor of the acceptance of independent autonomous vehicles. The most significant factor of the cooperative vehicle-highway autonomous vehicles’ acceptance is effort expectation. Additionally, risks, performance, existing traffic conditions, and personal innovation awareness also significantly affect autonomous driving acceptance. The research results can provide a theoretical basis for technology developers and industry managers to develop autonomous driving technology and policymaking.

scholarly journals Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3109
Author(s):  
Roïya Souissi ◽  
Ahmad Al Bitar ◽  
Mehrez Zribi
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Soil Texture ◽  
Surface Soil ◽  
Root Zone ◽  
Data Filtering ◽  
Surface Soil Moisture ◽  
The Mean ◽  
Artificial Neural

This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future.

scholarly journals Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Shuang Zheng ◽  
Xiaomei Hu
Keyword(s):  
Public Health ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Early Warning ◽  
Structural Equation ◽  
Equation Model ◽  
Hybrid Network ◽  
Public Health Emergencies ◽  
Artificial Neural

The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model (SEM) is constructed to determine the relationship between hidden and explicit variables by determining the relevant indicators and parameter estimation. The prediction model based on Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) is constructed. The method’s effectiveness is verified by comparing the prediction model’s loss value and accuracy in training and testing. Meanwhile, 50 pieces of actual cases are tested, and the warning level is determined according to the T-value. The results show that comparing and analyzing ANN, CNN, and the hybrid network of ANN and CNN, the hybrid network’s accuracy (95.1%) is higher than the other two algorithms, 89.1 and 90.1%. Also, the hybrid network has sound prediction effects and accuracy when predicting actual cases. Therefore, the early warning method based on ANN in deep learning has better performance in public health emergencies’ early warning, which is significant for improving early warning capabilities.

scholarly journals Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qingyan Meng ◽  
Linlin Zhang ◽  
Qiuxia Xie ◽  
Shun Yao ◽  
Xu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Landsat 8 ◽  
Combined Use ◽  
Artificial Neural ◽  
Agricultural Areas ◽  
Aperture Radar

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

scholarly journals A model for soil moisture dynamics estimation based on artificial neural network

2019 ◽  
Vol 81 ◽  
pp. 01017
Author(s):  
Wei Wang ◽  
Shuya Wang ◽  
Jianxia Chang ◽  
Dan Bai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Water Diversion ◽  
Soil Moisture Dynamics ◽  
Growth Environment ◽  
Proposed Model ◽  
Solid Foundation ◽  
Artificial Neural ◽  
Moisture Dynamics

Research on soil moisture estimation models can effectively improve the growth environment of crops. In this paper, the author studied the artificial neural network and variation pattern of soil moisture. Then, application of the model for water diversion estimation was explored based on artificial neural network. On this basis, an optimization algorithm was presented to simulate water diversion. Furthermore, a model for remote sensing of soil moisture dynamics was applied to artificial neural network. It has been proven that the research can optimize the application of the proposed model, laying a solid foundation for future study.

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