Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network
Reuse of recycled water is very important to both the environment and economy, while the public cognition degree towards recycled water reuse also plays a key role in this process, and it determines the acceptance degree of the public towards recycled water reuse. Under the background of the big dat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1876861 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849304633312280576 |
|---|---|
| author | Hanliang Fu Zhijian Liu Mengmeng Wang Zelin Wang |
| author_facet | Hanliang Fu Zhijian Liu Mengmeng Wang Zelin Wang |
| author_sort | Hanliang Fu |
| collection | DOAJ |
| description | Reuse of recycled water is very important to both the environment and economy, while the public cognition degree towards recycled water reuse also plays a key role in this process, and it determines the acceptance degree of the public towards recycled water reuse. Under the background of the big data, the Hadoop platform was used to collect and save data about the public’s cognition towards recycled water in one city and the BP neural network algorithm was used to construct an evaluation model that could affect the public’s cognition level. The public’s risk perception, subjective norm, and perceived behavioral control regarding recycled water reuse were selected as key factors. Based on a multivariate clustering algorithm, MATLAB software was used to make real testing on massive effective data and assumption models, so as to analyze the proportion of three evaluation factors and understand the simulation parameter scope of the cognition degree of different groups of citizens. Lastly, several suggestions were proposed to improve the public’s cognition on recycled water reuse based on the big data in terms of policy mechanism. |
| format | Article |
| id | doaj-art-3bbb95e6303b45dd9630eb7c2fd0fabc |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3bbb95e6303b45dd9630eb7c2fd0fabc2025-08-20T03:55:41ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/18768611876861Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural NetworkHanliang Fu0Zhijian Liu1Mengmeng Wang2Zelin Wang3School of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi Province 710055, ChinaDepartment of Power Engineering, North China Electric Power University, Baoding, Hebei Province 071003, ChinaSchool of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi Province 710055, ChinaSchool of Humanities and Social Sciences, Xi’an Jiaotong University, Xi’an, Shaanxi Province 710049, ChinaReuse of recycled water is very important to both the environment and economy, while the public cognition degree towards recycled water reuse also plays a key role in this process, and it determines the acceptance degree of the public towards recycled water reuse. Under the background of the big data, the Hadoop platform was used to collect and save data about the public’s cognition towards recycled water in one city and the BP neural network algorithm was used to construct an evaluation model that could affect the public’s cognition level. The public’s risk perception, subjective norm, and perceived behavioral control regarding recycled water reuse were selected as key factors. Based on a multivariate clustering algorithm, MATLAB software was used to make real testing on massive effective data and assumption models, so as to analyze the proportion of three evaluation factors and understand the simulation parameter scope of the cognition degree of different groups of citizens. Lastly, several suggestions were proposed to improve the public’s cognition on recycled water reuse based on the big data in terms of policy mechanism.http://dx.doi.org/10.1155/2018/1876861 |
| spellingShingle | Hanliang Fu Zhijian Liu Mengmeng Wang Zelin Wang Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network Complexity |
| title | Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network |
| title_full | Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network |
| title_fullStr | Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network |
| title_full_unstemmed | Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network |
| title_short | Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network |
| title_sort | big data digging of the public s cognition about recycled water reuse based on the bp neural network |
| url | http://dx.doi.org/10.1155/2018/1876861 |
| work_keys_str_mv | AT hanliangfu bigdatadiggingofthepublicscognitionaboutrecycledwaterreusebasedonthebpneuralnetwork AT zhijianliu bigdatadiggingofthepublicscognitionaboutrecycledwaterreusebasedonthebpneuralnetwork AT mengmengwang bigdatadiggingofthepublicscognitionaboutrecycledwaterreusebasedonthebpneuralnetwork AT zelinwang bigdatadiggingofthepublicscognitionaboutrecycledwaterreusebasedonthebpneuralnetwork |