On the possibility of calibrating urban storm-water drainage models using gauge-based adjusted radar rainfall estimates

Traditionally, urban storm water drainage models have been calibrated using only raingauge data, which may result in overly conservative models due to the lack of spatial description of rainfall. With the advent of weather radars, radar rainfall estimates with higher temporal and spatial resolution have become increasingly available and have started to be used operationally for urban storm water model calibration and real time operation. Nonetheless, the insufficient accuracy of radar rainfall estimates has proven problematic and has hindered its widespread practical use. This work explores the possibility of improving the applicability of radar rainfall estimates to the calibration of urban storm-water drainage models by employing gauge-based radar rainfall adjustment techniques. Four different types of rainfall estimates were used as input to the recently verified urban storm water drainage models of the Beddington catchment in South London; these included: raingauge, block-kriged raingauge, radar (UK Met Office Nimrod) and the adjusted (or merged) radar rainfall estimates. The performance of the simulated flow and water depths was assessed using measurements from 78 gauges. Results suggest that a better calibration could be achieved by using the block-kriged raingauge and the adjusted radar estimates as input, as compared to using only radar or raingauge estimates

Stochastic evaluation of sewer inlet capacity on urban pluvial flooding

In this paper we present an innovative methodology to stochastically assess the impact of sewer inlet conditions on urban pluvial flooding. The results showed that sewer inlet capacity can have a large impact on the occurrence of urban pluvial flooding. The methodology is a useful tool for dealing with uncertainties in sewer inlet operational conditions and contribute to comprehensive assessment of urban pluvial risk assessment

Semi-direct tree reconstruction using terrestrial LiDAR point cloud data

A new method was developed for reconstructing the geometric structure of large plants such as trees at the leaf-scale by utilizing terrestrial LiDAR data. The primary goal of the work was to develop a feasible means for accurately and rapidly reconstructing or “digitizing” entire trees in order to specify the position, orientation, and size of every leaf in digital tree models that provide geometric inputs for high-resolution biophysical models or analyses. As with any optical measurement technique, a primary challenge is accurately accounting for plant matter that is occluded from view of the sensor. The present method is termed “semi-direct” because it uses a triangulation procedure to approximately directly reconstruct as many leaves as possible that are in view of the scanner. For plant matter obstructed from view, a statistical backfilling procedure was used to add additional leaves such that the three-dimensional distribution of leaf area and orientation of the reconstructed plant matched that of the actual plant on average. In a best case scenario such as when leaf density is low, nearly all leaf area is directly reconstructed from the scan and the branch and clumping structure is preserved within the reconstruction. In the worst case scenario such as when the leaf density is very high and nearly all leaves are occluded from view of the scanner, only a small fraction of leaves can be directly reconstructed, but at a minimum the distribution of leaf area and the leaf angle distribution across the reconstructed plant will be consistent with that of the actual plant. Unlike many other approaches, the present method does not rely on the woody matter of the plant to provide a skeleton for reconstruction, and can be used in dense plants where little woody matter is visible from the scanner

The Local Optima Level in Chemotherapy Schedule Optimisation

In this paper a multi-drug Chemotherapy Schedule Optimisation Problem (CSOP) is subject to Local Optima Network (LON) analysis. LONs capture global patterns in fitness landscapes. CSOPs have not previously been subject to fitness landscape analysis. We fill this gap: LONs are constructed and studied for meaningful structure. The CSOP formulation presents novel challenges and questions for the LON model because there are infeasible regions in the fitness landscape and an unknown global optimum; it also brings a topic from healthcare to LON analysis. Two LON Construction algorithms are proposed for sampling CSOP fitness landscapes: a Markov-Chain Construction Algorithm and a Hybrid Construction Algorithm. The results provide new insight into LONs of highly-constrained spaces, and into the proficiency of search operators on the CSOP. Iterated Local Search and Memetic Search, which are the foundations for the LON algorithms, are found to markedly out-perform a Genetic Algorithm from the literature