This study aims to assess the upstream rainfall thresholds corresponding to the maximum allowable turbidity of source water, using monitoring data and artificial neural network computation. The Taipei Water Source Domain was selected as the study area, and the upstream rainfall records were collected for statistical analysis. Using analysis of variance (ANOVA), the cumulative rainfall records of one-day Ping-lin, two-day Ping-lin, two-day Tong-hou, one-day Guie-shan, and one-day Tai-ping (rainfall in the previous 24 or 48 hours at the named weather stations) were found to be the five most significant parameters for downstream turbidity development. An artificial neural network model was constructed to predict the downstream turbidity in the area investigated. The observed and model-calculated turbidity data were applied to assess the rainfall thresholds in the studied area. By setting preselected turbidity criteria, the upstream rainfall thresholds for these statistically determined rain gauge stations were calculated.
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