Vernon, James P听1听;听Raseman, William J听2听;听Kasprzyk, Joseph R听3听;听Rosario-Ortiz, Fernando听4听;听Hohner, Amanda K听5听;听Summers, R S听6
1听麻豆影院
2听麻豆影院
3听麻豆影院
4听麻豆影院
5听麻豆影院
6听麻豆影院
Forested watersheds contain high quality, high quantity source waters that approximately 180 million Americans rely on for their drinking water supply. These forested water supplies are vulnerable to water quality changes due to wildfires, which tend to increase levels of suspended sediment, nutrients, organic carbon, and heavy metals in source waters, and lead to subsequent health risks to consumers. Based on climatic changes and forest fuel buildup due to forest management practices, areas of the United States prone to wildfires may experience an increase in wildfire frequency and severity. At present, water treatment plant (WTP) managers have few tools to predict how their source water quality will be affected by a wildfire and whether their current WTP can handle these variations from normal operations. In this research, we explore how a multi-objective evolutionary algorithm (MOEA) can be used in conjunction with the USEPA Water Treatment Plant Model to suggest robust management strategies for WTPs and predict changes in finished drinking water quality due to wildfire events. To represent wildfire conditions, we use water quality data from the 2012 High Park fire in Fort Collins, CO. Based on these data, we perform simulations to determine the sensitivity of management practices produced by the MOEA to variations in wildfire conditions. In future work, the impact of other extreme events, such as flooding and drought, will also be considered.