TANG Hanqing, ZHANG Tuqiao, YANG Yulong, et al. Prediction of mixed water supply zone by spatial modified interpolation of characteristic water quality index[J]. 2021, 53(11): 29-36.
DOI:
TANG Hanqing, ZHANG Tuqiao, YANG Yulong, et al. Prediction of mixed water supply zone by spatial modified interpolation of characteristic water quality index[J]. 2021, 53(11): 29-36.DOI: 10.11918/202001087.
Prediction of mixed water supply zone by spatial modified interpolation of characteristic water quality index
There are mixed water supply zones between different water plants in multi-source water supply areas
where the hydraulic water quality conditions are not stable
and water quality problems such as excessive iron release and low residual chlorine are easy to occur. The mixed water quality risk area can be predicted by using the spatial modified interpolation method of characteristic water quality index. Characteristic water quality index refers to the index with distinct identifying characteristics
which are distributed regularly in the water distribution system and have large differences in the finished water of different water plants. According to its spatial distribution
the separate water supply area and mixed water supply area of each water plant can be determined. However
there are great differences between the distribution of pipe network and the geographical space
so the correction coefficient should be introduced before interpolation to correct the characteristic index. In this study
the mixed water supply zone of H city was predicted. Results show that the spatial modified interpolation results of the characteristic water quality index conductivity and total iron concentration of H city were basically consistent
and accorded with the results of hydraulic model simulation. Online monitoring points were set in the predicted mixed water supply zone
and frequent changes in flow velocity and flow direction were observed
which conformed to the hydraulic characteristics of the mixed water supply zone. It indicates that the prediction results are correct and the method is feasible.