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Urbanization is leading us to a more chaotic state where healthy living becomes a prime concern. The high-rise buildings influence the urban setting with a high shadow rate on surroundings that can have no positive impact on the general neighborhood. Nevertheless, shadows are the main factor of defeatist virtual settings, they are expensive to render in real-time. This paper investigates how the amount of sunlight varies by season and how seasons can indicate the time of year to understand how shadows vary in length at different times of the day and how they change over the seasons. We propose a novel efficient (fast and scalable) algorithm to calculate a 2.5D cast-shadow map from a given LiDAR-derived Digital Surface Model (DSM). We present a proof-of-concept demonstration to examine the technical practicability of the introduced algorithm. Tensor-based techniques such as singular value decomposition, tensor unfolding are examined and deployed to represent the multidimensional data. The proposed method exploits horizon mapping ideas and extends the method to a modern graphics algorithm (Bresenham's line drawing algorithm) to account for the DSM's underlying surface geometry. A proof-of-concept is developed utilizing Python's TensorFlow library, exploring data flow graphs and the tensor data structure. The heavy computer graphics algorithm used in this paper is parallelized using PySpark. Results explicate significant enhancements in overall performance while preserving accuracy at negligible variations. [ABSTRACT FROM AUTHOR]

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89). [ABSTRACT FROM AUTHOR]

Integrated environmental modelling requires coupling sub-models at different spatial and temporal scales, thus accounting for change of support procedures (aggregation and disaggregation). We introduce the R-package spatio-temporal Uncertainty Propagation across multiple scales, stUPscales, which constitutes a contribution to state-of-the-art open source tools that support uncertainty propagation analysis in temporal and spatio-temporal domains. We illustrate the tool with an uncertainty propagation example in environmental modelling, specifically in the urban water domain. The functionalities of the class setup and the methods and functions MC.setup, MC.sim, MC.analysis and Agg.t are explained, which are used for setting up, running and analysing Monte Carlo uncertainty propagation simulations, and for spatio-temporal aggregation. We also show how the package can be used to model and predict variables that vary in space and time by using a spatio-temporal variogram model and space-time ordinary kriging. stUPscales takes uncertainty characterisation and propagation a step further by including temporal and spatio-temporal auto- and cross-correlation, resulting in more realistic (spatio-)temporal series of environmental variables. Due to its modularity, the package allows the implementation of additional methods and functions for spatio-temporal disaggregation of model inputs and outputs, when linking models across multiple space-time scales. [ABSTRACT FROM AUTHOR]

Many complex urban drainage quality models are computationally expensive. Complexity and computing times may become prohibitive when these models are used in a Monte Carlo (MC) uncertainty analysis of long time series, in particular for practitioners. Computationally scalable and fast “surrogate” models may reduce the overall computation time for practical applications in which often large data sets would be needed otherwise. We developed a simplified semi-distributed urban water quality model, EmiStatR, which brings uncertainty and sensitivity analyses of urban drainage water quality models within reach of practitioners. Its lower demand in input data and its scalability allow for simulating water volume and pollution loads in combined sewer overflows in several catchments fast and efficiently. The scalable code implemented in EmiStatR reduced the computation time significantly (by a factor of around 24 when using 32 cores). EmiStatR can be applied efficiently to test hypotheses by using MC uncertainty studies or long-term simulations. [ABSTRACT FROM AUTHOR]