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In this paper, the main features and performances of a new numerical scheme, ILILPM (Improved Locally and Instantaneously Linearized Parabolic Model) are described. ILILPM is an improved version of the Parabolic and Backwater (PAB) and Linearized Parabolic Model (LPM) schemes, proposed in literature for the approximate solution of the Parabolic Wave model. The algorithm presented is able to take into account transcritical flow regime and transitions from free-surface to pressurized flow in tree-like channel networks. Due to its unconditional stability, the model allows large computational time steps, leading to very fast simulations during transients for a class of flow conditions larger than those solved by the parent schemes. The model is demonstrated by comparing its results with experimental observations and with the results provided by the numerical solution of the full De Saint-Venant equations. [ABSTRACT FROM AUTHOR]

An assessment has been carried out to study the performance of seven different LiDAR filtering algorithms and to evaluate their suitability for urban flood modeling applications. It was found that none of these algorithms can be regarded as fully suitable to support such work in its present form. The paper presents the augmentation of an existing Progressive Morphological filtering algorithm for processing raw LiDAR data to support a 1D/2D urban flood modeling framework. The existing progressive morphological filtering algorithm was modified to incorporate buildings with basement, passage buildings and solid buildings and its value was demonstrated on a case study from Kuala Lumpur, Malaysia. The model results were analyzed and compared against recorded data in terms of flood depths, flood extents and flood velocities. The difference in flood depths of approximately 40% was observed between a model that uses a DTM modified by the progressive morphological filtering algorithm and the predictions of other models. The overall results suggest that incorporation of building basements within the DTM can lead to a significant difference in the model results with a tendency towards overestimation for those models which do not incorporate such a feature. [ABSTRACT FROM AUTHOR]

This paper describes a simple mathematical technique that uses a genetic algorithm and least squares optimization to obtain a functional approximation (or computer program) for a given data set. Such an optimal functional form is derived from a pre-defined general functional formulation by selecting optimal coefficients, decision variable functions, and mathematical operators. In the past, functional approximations have routinely been obtained through the use of linear and non-linear regression analysis. More recent methods include the use of genetic algorithms and genetic programming. An example application based on a data set extracted from the commonly used Moody diagram has been used to demonstrate the utility of the proposed method. The purpose of the application was to determine an explicit expression for friction factor and to compare its performance to other available techniques. The example application results in the development of closed form expressions that can be used for evaluating the friction factor for turbulent pipe flow. These expressions compete well in accuracy with other known methods, validating the promise of the proposed method in identifying useful functions for physical processes in a very effective manner. The proposed method is simple to implement and has the ability to generate simple and compact explicit expressions for a given response function. [ABSTRACT FROM AUTHOR]