An approach to quality validation of large-scale data from the Chinese Flash Flood Survey and Evaluation (CFFSE)

Abstract Quality control of large-scale flash flood survey and evaluation data is vital and refers to various social and natural factors. In this study, we present a quality validation approach that uses a data model, Anselin Local Moran’s I (DM-Moran), which is based on a model of the flash flood data and a spatial data mining algorithm. The approach of the DM-Moran model involves examining logical relationships and detecting anomalous survey units, which effectively integrates the advantages of certainty rules and checking for reasonableness. It resolves the inconsistencies in massive amounts of flash flood survey data that result from inconsistencies. We used the DM-Moran model to validate the quality of the data of the Chinese Flash Flood Survey and Evaluation (CFFSE) project. The kappa coefficients of the two steps of this approach were 0.95 and 0.99, which meet the requirements of the CFFSE project. We consider the DM-Moran model an effective approach to checking the quality of various other large-scale disaster datasets.