A three-layer Artificial Neural Network (ANN) model was developed to forecast air pollution levels. The subsequent SO 2 concentration (24-hour averaged) being the output parameter of this study was estimated by seven input parameters such as preceding SO 2 concentrations (24-hour averaged), average daily temperature, sea-level pressure, relative humidity, cloudiness, average daily wind speed and daily dominant wind direction. After Backpropagation training combined with Principal Component Analysis (PCA), the proposed model predicted subsequent SO 2 values based on measured data. ANN testing outputs were proven to be satisfactory with correlation coefficients of about 0.770, 0.744 and 0.751 for the winter, summer and overall data, respectively.
This article adds to the literature on the investigation of water use behavior of people under their daily routines. A self-administrated survey of water users was conducted for both graduate and undergraduate students at Boğaziçi University, Turkey in 2019. This study quantifies and maps the water footprint (WF) of Boğaziçi University (BU). It reports preferences of students and personnel in terms of indoor water use, outdoor water use, and virtual water use such as transportation, shopping, and dietary preferences. Water footprints are estimated per person for both engineering students and all students of BU. WF of an average BU student is above the average WF of Turkey as well as the average global WF. The attributes that influence the water use behavior of people were broadly categorized into two groups, which were dietary preferences and shopping preferences. Moreover, the eating pattern of a person regarding whether a person consumes meat was the largest contributor to the WF of BU. The results of this study can help to develop a basic understanding of WF and how it is affected by people's choices. This study is also unique by calculating water footprint in terms of direct and virtual water footprint by considering people’s daily choices in Turkey.
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