Vladan Babovic

On inducing equations for vegetation resistance

The paper describes the process of induction of equations for the description of vegetation-induced roughness from several angles. Firstly, it describes two approaches for obtaining theoretically well-founded analytical expressions for vegetation resistance. The first of the two is based on simplified assumptions for the vertical flow profile through and over vegetation, whereas the second is based on an analytical solution to the momentum balance for flow through and over vegetation. In addition to analytical expressions the paper also outlines a numerical 1-DV k–e turbulence model which includes several important features related to the influence plants exhibit on the flow. Last but not least, the paper presents a novel way of applying genetic programming to the results of the 1-DV model, in order to obtain an expression for roughness based on synthetic data. The resulting expressions are evaluated and compared with an independent data set of flume experiments

A data mining approach to modelling of water supply assets

Abstract The economic and social costs associated with pipe bursts and associated leakage problems in modern water supply systems are rapidly rising to unacceptably high levels. Pipe burst risks depend on a number of factors which are extremely difficult to characterise. A part of the problem is that water supply assets are mainly situated underground, and therefore not visible and under influence of various highly unpredictable forces. This paper proposes the use of advanced data mining methods in order to determine the risks of pipe bursts. For example, analysis of the database of already occurred bursts events can be used to establish a risk model as a function of associated characteristics of bursting pipe (its age, diameter, material of which it is built, etc.), soil type in which a pipe is laid, climatological factors (such as temperature), traffic loading, etc. In addition to the immediate aid with the the choice of pipes to be replaced, the outlined approach opens completely new avenues in asset management: the one of asset modeling. The condition of an asset such as a water supply network deteriorates with age. With reliable risk models, addressing the evolution of risk with aging asset, it is now possible to plan optimal rehabilitation strategies in advance, before the burst actually occurs.

Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff - Introductory Investigations

The decomposition of regression error into bias and variance terms provides insight into the generalization capability of modeling methods. The paper offers an introduction to bias/variance decomposition of mean squared error, as well as a presentation of experimental results of the application of genetic programming. Finally ensemble methods such as bagging and boosting are discussed that can reduce the generalization error in genetic programming.

Velocity predictions in compound channels with vegetated floodplains using genetic programming

Abstract Data collection and storage methods have improved vastly over recent years, however the processes of information and knowledge extraction from data have not mirrored this. The application of computer supported scientific knowledge discovery processes to carefully collected observations aims to improve the understanding of the processes that generated or produced these data. In this paper, these new techniques have been applied to the complex and poorly understood phenomena of flow through idealised vegetation. The ability to predict, with improved accuracy, velocities within wetlands and other vegetated areas would be advantageous as these regions are increasingly being recognised for their natural flood alleviation properties. In this study, laboratory data collected in a flume with steady flows over a deep channel with relatively shallow vegetated floodplains were used to induce the formulation of expressions using a data driven discovery technique, namely genetic programming (GP). The objective of the study was not only to gain an understanding of the effect of vegetation on velocity distributions across a channel but moreover to demonstrate an alternative discovery process. The performance of the genetic program is reported for three variations of the GP. The reported results of the experiments were found to be encouraging and further work is detailed.

Declarative and Preferential Bias in GP-based Scientific Discovery

This work examines two methods for evolving dimensionally correct equations on the basis of data. It is demonstrated that the use of units of measurement aids in evolving equations that are amenable to interpretation by domain specialists. One method uses a strong typing approach that implements a declarative bias towards correct equations, the other method uses a coercion mechanism in order to implement a preferential bias towards the same objective. Four experiments using real-world, unsolved scientific problems were performed in order to examine the differences between the approaches and to judge the worth of the induction methods.Not only does the coercion approach perform significantly better on two out of the four problems when compared to the strongly typed approach, but it also regularizes the expressions it induces, resulting in a more reliable search process.A trade-off between type correctness and ability to solve the problem is identified. Due to the preferential bias implemented in the coercion approach, this trade-off does not lead to sub-optimal performance. No evidence is found that the reduction of the search space achieved through declarative bias helps in finding better solutions faster. In fact, for the class of scientific discovery problems the opposite seems to be the case.

Valuing flexibilities in the design of urban water management systems.

Climate change and rapid urbanization requires decision-makers to develop a long-term forward assessment on sustainable urban water management projects. This is further complicated by the difficulties of assessing sustainable designs and various design scenarios from an economic standpoint. A conventional valuation approach for urban water management projects, like Discounted Cash Flow (DCF) analysis, fails to incorporate uncertainties, such as amount of rainfall, unit cost of water, and other uncertainties associated with future changes in technological domains. Such approach also fails to include the value of flexibility, which enables managers to adapt and reconfigure systems over time as uncertainty unfolds. This work describes an integrated framework to value investments in urban water management systems under uncertainty. It also extends the conventional DCF analysis through explicit considerations of flexibility in systems design and management. The approach incorporates flexibility as intelligent decision-making mechanisms that enable systems to avoid future downside risks and increase opportunities for upside gains over a range of possible futures. A water catchment area in Singapore was chosen to assess the value of a flexible extension of standard drainage canals and a flexible deployment of a novel water catchment technology based on green roofs and porous pavements. Results show that integrating uncertainty and flexibility explicitly into the decision-making process can reduce initial capital expenditure, improve value for investment, and enable decision-makers to learn more about system requirements during the lifetime of the project.

Ripple Crossover in Genetic Programming

This paper isolates and identifies the effects of the crossover operator used in Grammatical Evolution. This crossover operator has already been shown to be adept at combining useful building blocks and to outperform engineered crossover operators such as Homologous Crossover. This crossover operator, Ripple Crossover is described in terms of Genetic Programming and applied to two benchmark problems.Its performance is compared with that of traditional sub-tree crossover on populations employing the standard functions and terminal set, but also against populations of individuals that encode Context Free Grammars. Ripple crossover is more effective in exploring the search space of possible programs than sub-tree crossover. This is shown by examining the rate of premature convergence during the run. Ripple crossover produces populations whose fitness increases gradually over time, slower than, but to an eventual higher level than that of sub-tree crossover.

Reducing Risk Through Real Options in Systems Design: The Case of Architecting a Maritime Domain Protection System

Complex engineering systems are usually designed to last for many years. Such systems will face many uncertainties in the future. Hence the design and deployment of these systems should not be based on a single scenario, but should incorporate flexibility. Flexibility can be incorporated in system architectures in the form of options that can be exercised in the future when new information is available. Incorporating flexibility comes, however, at a cost. To evaluate if this cost is worth the investment a real options analysis can be carried out. This approach is demonstrated through analysis of a case study of a previously developed static system-of-systems for maritime domain protection in the Straits of Malacca. This article presents a framework for dynamic strategic planning of engineering systems using real options analysis and demonstrates that flexibility adds considerable value over a static design. In addition to this it is shown that Monte Carlo analysis and genetic algorithms can be successfully combined to find solutions in a case with a very large number of possible futures and system designs.

An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions

To address the issue of decision support for designing and managing flexible projects and systems in the face of uncertainties, this paper integrates real options valuation, decision analysis techniques, Monte Carlo simulations and evolutionary algorithms in an evolutionary real options framework. The proposed evolutionary real options framework searches for an optimized portfolio of real options and makes adaptive plans to cope with uncertainties as the future unfolds. Exemplified through a test case, the evolutionary framework not only compares favorably with traditional fixed design approaches but also delivers considerable improvements over prevailing real options practices.

Data Mining and Knowledge Discovery in Sediment Transport

The means for data collection have never been as advanced as they are today. Moreover, the numerical models we use today have never been so advanced. Feeding and calibrating models against collected measurements, however, represents only a one-way flow: from measurements to the model. The observations of the system can be analyzed further in the search for the information they encode. Such automated search for models accurately describing data constitutes a new direction that can be identified as that of data mining. It can be expected that in the years to come we shall concentrate our efforts more and more on the analysis of the data we acquire from natural or artificial sources and that we shall mine for knowledge from the data so acquired. Data mining and knowledge discovery aim at providing tools to facilitate the conversion of data into a number of forms, such as equations, that provide a better understanding of the process generating or producing these data. These new models combined with the already available understanding of the physical processes—the theory—result in an improved understanding and novel formulations of physical laws and improved predictive capability. This article describes the data mining process in general, as well as an application of a data mining technique in the domain of sediment transport. Data related to the concentration of suspended sediment near a bed are analyzed by the means of genetic programming. Machine-induced relationships are compared against formulations proposed by human experts and are discussed in terms of accuracy and physical interpretability.