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Belowground urban stormwater networks (BUSNs) are critical for removing excess rainfall from impervious urban areas and preventing or mitigating urban flooding. However, available BUSN data are sparse, preventing the modeling and analysis of urban hydrologic processes at regional and larger scales. We propose a novel algorithm for estimating BUSNs by drawing on concepts from graph theory and existing, extensively available land surface data, such as street network, topography, and land use/land cover. First, we derive the causal relationships between the topology of BUSNs and urban surface features based on graph theory concepts. We then apply the causal relationships and estimate BUSNs using web-service data retrieval, spatial analysis, and high-performance computing techniques. Finally, we validate the derived BUSNs in the metropolitan areas of Los Angeles, Seattle, Houston, and Baltimore in the US, where real BUSN data are partly available to the public. Results show that our algorithm can effectively capture 59 %–76 % of the topology of real BUSN data, depending on the supporting data quality. This algorithm has promising potential to support large-scale urban hydrologic modeling and future urban drainage system planning. [ABSTRACT FROM AUTHOR]

The angularity of the coarse aggregate (CA) affects the performance of the concrete. A digital microscope is used to obtain three-dimensional contour data points on the surface of a CA and thus improve the effectiveness of the evaluation of the angularity of the CA. Three methods of characterizing the angularity of the CA, namely applying a fitted ellipsoid model (AIf), steradian model (AIc), or roughness model (SR), were studied. The effectiveness of different angularity characterization methods was compared with the Los Angeles abrasion (LAA) and void content (VC) experiments. Results show that the angularity index of the fitted ellipsoid model is the most effective model in characterizing aggregate angularity, and it can eliminate the influence of the particle sizes; the angularity index AIf changes most rapidly with an increase in the VC, indicating that AIf is the most effective in characterizing the VC of the CA; and the angularity index AIc has a strong negative correlation with sphericity, which indicates that AIc is the most effective index in characterizing the sphericity of the CA. A new algorithm (fitted ellipsoid model) for evaluating the 3D angularity of aggregates has significant advantages. The angularity index AIf is more suitable for characterizing the void content of the aggregate. The angularity index AIc is more suitable for characterizing the sphericity of the aggregate. [ABSTRACT FROM AUTHOR]

Estimation using a suboptimal method can lead to imprecise models, with cascading effects in complex models, such as climate change or pollution. The goal of this study is to compare the solutions supplied by different algorithms used to model ozone pollution. Using Box and Tiao (1975) study, we have predicted ozone concentration in Los Angeles with an ARIMA and an autoregressive process. We have solved the ARIMA process with three algorithms (i.e., maximum likelihood, like Box and Tiao, conditional least square and unconditional least square) and the autoregressive process with four algorithms (i.e., Yule–Walker, iterative Yule–Walker, maximum likelihood, and unconditional least square). Our study shows that Box and Tiao chose the appropriate algorithm according to the AIC but not according to the mean square error. Furthermore, Yule–Walker, which is the default algorithm in many software, has the least reliable results, suggesting that the method of solving complex models could alter the findings. Finally, the model selection depends on the technical details and on the applicability of the model, as the ARIMA model is suitable from the AIC perspective but an autoregressive model could be preferred from the mean square error viewpoint. Our study shows that time series analysis should consider not only the model shape but also the model estimation, to ensure valid results. [ABSTRACT FROM AUTHOR]