Deep neural network analyses of water quality time series associated with water sensitive urban design (WSUD) features

2020 ◽  
Vol 8 (4) ◽  
pp. 313-332
Author(s):  
Ho Huu Loc ◽  
Quang Hung Do ◽  
A.A. Cokro ◽  
Kim N. Irvine
2010 ◽  
Vol 113-116 ◽  
pp. 1367-1370 ◽  
Author(s):  
Bin Sheng Liu ◽  
Ying Wang ◽  
Xue Ping Hu

There are many ways to predict drinking water quality such as neural network, gray model, ARIMA. But the prediction precise is need to improve. This paper proposes a new forecast method according the characteristic of drinking water quality and the evidence showed that the prediction is effectively. So it is able to being used in actual prediction.


2020 ◽  
Vol 10 (22) ◽  
pp. 8142
Author(s):  
Yanlei Gu ◽  
Takuya Shibukawa ◽  
Yohei Kondo ◽  
Shintaro Nagao ◽  
Shunsuke Kamijo

Stock performance prediction is one of the most challenging issues in time series data analysis. Machine learning models have been widely used to predict financial time series during the past decades. Even though automatic trading systems that use Artificial Intelligence (AI) have become a commonplace topic, there are few examples that successfully leverage the proven method invented by human stock traders to build automatic trading systems. This study proposes to build an automatic trading system by integrating AI and the proven method invented by human stock traders. In this study, firstly, the knowledge and experience of the successful stock traders are extracted from their related publications. After that, a Long Short-Term Memory-based deep neural network is developed to use the human stock traders’ knowledge in the automatic trading system. In this study, four different strategies are developed for the stock performance prediction and feature selection is performed to achieve the best performance in the classification of good performance stocks. Finally, the proposed deep neural network is trained and evaluated based on the historic data of the Japanese stock market. Experimental results indicate that the proposed ranking-based stock classification considering historical volatility strategy has the best performance in the developed four strategies. This method can achieve about a 20% earning rate per year over the basis of all stocks and has a lower risk than the basis. Comparison experiments also show that the proposed method outperforms conventional methods.


2018 ◽  
Vol 115 (33) ◽  
pp. E7690-E7699 ◽  
Author(s):  
Jacki Schirmer ◽  
Fiona Dyer

The ongoing challenge of maintaining and improving the quality of water that leaves urban stormwater systems is often addressed using technical rather than social solutions. The need for investment in often expensive water infrastructure can be reduced through better investing in promoting human behaviors that protect water quality as part of water-sensitive urban design (WSUD) initiatives. Successfully achieving this requires understanding factors that influence adoption of proenvironmental behaviors. We review past studies examining this topic and identify that factors influencing adoption of proenvironmental behaviors relevant to WSUD commonly fall into four domains: proenvironmental values and norms, awareness and knowledge of environmental problems and the actions that can address them, proximity and place-based identity, and life-stage and lifestyle factors. We propose the VAIL (values, awareness, identify, lifestyle) framework, based on these four domains and able to be contextualized to specific water-quality problems and individual communities, to assist in diagnosing factors influencing adoption of proenvironmental behaviors. We demonstrate the applicability of the framework in a case study examining adoption of gardening practices that support water quality in Canberra, Australia. We developed 22 locally relevant VAIL indicators and surveyed 3,334 residents to understand engagement in four water-friendly gardening behaviors that help improve water quality in local lakes. In regression modeling, the indicators explained a significant amount of variance in these behaviors and suggested avenues for supporting greater adoption of these behaviors. Predictor variables across all four VAIL domains were significant, highlighting the importance of a multidomain framework.


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