![]() We utilize various datasets from, opendata.nhs and Demographics and Health Surveys (DHS) Program to observe the variations in the quality score and formulate a label using Principal Component Analysis(PCA). ![]() ![]() The current empirical study was undertaken to formulate a concrete automated data quality platform to assess the quality of incoming dataset and generate a quality label, score and comprehensive report. Mostly data quality is measured on an ad-hoc basis, and hence none of the developed concepts provide any practical application. Data Quality refers to the relevance of the information present and helps in various operations like decision making and planning in a particular organization. Missing feature data leads to overestimation of flood depth, while lower model resolution results in underestimating flood depth and overestimating flood extent and volume.ĭata is expanding at an unimaginable rate, and with this development comes the responsibility of the quality of data. Errors in flood depth, area and volume estimation are functions of both the data completeness and model resolution. The study found that the model may over or underestimate flood volume and duration with different levels of missing data depending on the parameters - roughness, diameter or depth, and that model performance is more sensitive to missing data that is downstream and closer to the outfall as opposed to missing data upstream. We ran simulations under the 50-year return period design storm and compared simulated flood metrics assuming the highest-resolution and complete data model configuration as a reference. These configurations were generated through random Monte Carlo sampling for SWMM 1D and selective sampling with four cases for SWMM 1D-2D. We tested several model configurations assuming different levels of (i) availability of stormwater infrastructure data (ranging from 5% to 75% of attribute-values missing) and (ii) terrain aggregation (i.e., 4.6 m and 9.7 m). Here, we have collected detailed infrastructure data, a high-resolution 0.3-m LiDAR-based digital elevation model, and catchment properties data. For this aim, we apply the one-dimensional (1D) and coupled one- and two-dimensional (1D-2D) versions of US Environmental Protection Agency’s Storm Water Management Model (SWMM) in an urban catchment in the city of Phoenix, Arizona. In this study, we quantify how the accuracy and precision of urban hydrologic-hydrodynamic models vary as a function of data completeness and model resolution. Unfortunately, cities often do not have or cannot release complete infrastructure data, and high-resolution terrain data products are not available everywhere. The accuracy of hydrologic and hydrodynamic models, used to study urban hydrology and predict urban flooding, depends on the availability of high-resolution terrain and infrastructure data.
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