Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network

Abstract

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.

PUBLIC ACCEPTANCE OF GREY WATER REUSE: A CASE STUDY & ANALYSIS IN THE UAE

“…The results of this study were found to support the findings of similar studies conducted in the UAE (Maraqa & Ghoudi, 2012), (Wait, 2014), (Amaris et al, 2020), (Chowdhury, 2009), (Fu et al, 2018), (Hyde et al, 2017). In addition, the results agree with the factors identified in the literature to have an impact on the public acceptance of grey water reuse.…”

“…Only 8% of the participants consider the matter not significant enough for their contribution. However, diseases and water quality concerns are the major causes of concerns, which agrees with the findings documented by (Fu et al, 2018) in their literary analysis. Social and psychological barriers concerned 27 % of the respondents, while 18 % rejected treated grey water for religious reasons as found by (Hyde et al, 2017).…”

“…Numerous studies were previously conducted to investigate the factors influencing public acceptance of treated grey water utilization on a domestic scale. A literature review conducted by Po, Kaercher and Nancarrow, in 2003, revealed a number of factors including perceptions of health risks associated with utilizing reused water, psychological discomfort, the specific use of the treated grey water, perception of fresh water abundance and availability, confidence in the quality of the treated water, scientific knowledge about the treatment process, attitude towards the environment and socioeconomic factors (Fu et al, 2018). Domènecha and Sauría developed a framework in 2010 to analyze the factors influencing the public acceptance of grey water reuse, summarizing these factors as perceived cost, perceived health risk, operational regime, and environmental awareness (Domnech & Saurí, 2010).…”

Digitalization in Urban Water Governance: Case Study of Bengaluru and Singapore

“…Similar digital tools and techniques such as Information and Communication Technology (ICT), AI and Big Data could be used for providing wastewater services which includes operation and maintenance of wastewater infrastructure-sewers and wastewater treatment plants (WWTPs) and their life cycle assessment (Sousa et al, 2014;Du et al, 2019;Rebello et al, 2021). Digital technology comes very handy even when it comes to water demand management or analysing and improving public perception for using recycled water (Jayarathna et al, 2017;Fu et al, 2018). ICT solutions can be used to provide integrated solutions for water supply, water demand management and asset management (Kulkarni and Farnham 2016).…”

Research on key technologies of intelligent transportation based on image recognition and anti-fatigue driving

“…The idea of KNN algorithm is to input test data when the training set data and labels are known [28], compare the characteristics of test data with those of training set, and find the first K data which is the most similar to the training set, then the corresponding category of the test data is the most frequent occurrence of K data. The classification of the algorithm is described as follows:…”