基于BP神经网络水质模型的城市安全距离研究——以芜湖和马鞍山为例

快速的城镇化使城市间距不断减小,上下游城市间水质的相互影响愈发显著.本研究提出基于水质的城市安全距离概念,建立基于BP神经网络水质模型的城市安全距离量化方法,并选择长江沿岸相邻的芜湖、马鞍山两市为案例,评估未来两市建成区扩张后城市间距的安全性,计算两市建成区的最小安全距离.结果显示,2020年芜湖与马鞍山4.6km的间距属于安全距离,能够保证下游城市马鞍山上游控制断面地表II类的水功能要求.但与2010年相比,控制断面水质变差,COD与氨氮浓度分别提高了29.2%与23.2%.为了保证控制断面的水功能要求,芜湖与马鞍山两市的最小安全距离为3.2km.

Safe distance between cities based on the BP neural network water quality model: a study on Wuhu and Ma'anshan

As a result of decreasing distance between cities during the rapid urbanization, the water quality of cities located in upper and lower reaches has closer interations. This paper proposes the concept of safe distance between cities to ensure the water quality in downstream cities, which is quantified based on the BP neural network model for water quality. Two adjacent cities along the Yangtze River, Wuhu and Ma'anshan, are chosen as the representative case to evaluate the safety of water quality and quantify the minimum safe distance after city expansion. The results reveal a safe distance of 4.6km between the two cities in 2020, which could ensure the water quality of the control section in upper reaches of Ma'anshan (the downstream city) to meet the class II standard of surface water. However, compared with the year 2010, the water quality of the control section will decline, where the COD concentration is projected to increase by 29.2% and NH₃-N by 23.2%. In order to ensure the water function of the control section, the minimum safe distance between the two cities needs to be 3.2km.