Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

Simulating the influences of groundwater on regional geomorphology using a distributed, dynamic, landscape evolution modelling platform

A dynamic landscape evolution modelling platform (CLiDE) is presented that allows a variety of Earth system interactions to be explored under differing environmental forcing factors. Representation of distributed surface and subsurface hydrology within CLiDE is suited to simulation at sub-annual to centennial time-scales. In this study the hydrological components of CLiDE are evaluated against analytical solutions and recorded datasets. The impact of differing groundwater regimes on sediment discharge is examined for a simple, idealised catchment, Sediment discharge is found to be a function of the evolving catchment morphology. Application of CLiDE to the upper Eden Valley catchment, UK, suggests the addition of baseflow-return from groundwater into the fluvial system modifies the total catchment sediment discharge and the spatio-temporal distribution of sediment fluxes during storm events. The occurrence of a storm following a period of appreciable antecedent rainfall is found to increase simulated sediment fluxes

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

Copyright © 2009 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software, Volume 24 Issue 4 (2009), DOI: 10.1016/j.envsoft.2008.09.013This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA-ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the 'full' fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA-ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA-ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution

Modelling Hen Harrier Dynamics to Inform Human-Wildlife Conflict Resolution : A Spatially-Realistic, Individual-Based Approach

Peer reviewedPublisher PD

Sustainable Design and Building Information Modelling: Case Study of Energy Plus House, Hieron's Wood, Derbyshire UK

In this paper the method for sustainable design analysis (SDA) integration with building information modelling (BIM) is explored, through the prism of a complex case study based research. BIM model federation and integration challenges are reported, including issues with combining geometry and managing attribute data. The research defines SDA as rapid and quantifiable analysis of multitude of sustainable alternatives and ‘what if’ questions posed by a design team during the early stages of the project, when the benefits of correct decisions can significantly exceed the actual investment required. The SDA concept and BIM integration findings are explained from conceptualisation to calculation stage, emphasising the importance of an iterative over a linear approach. The research approach adopted has led to more informed sustainable solutions at earlier stages of project development, with a generally lower level of development (LOD) and computational/modelling effort required

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone

Decision support system for the long-term city metabolism planning problem

A Decision Support System (DSS) tool for the assessment of intervention strategies (Alternatives) in an Urban Water System (UWS) with an integral simulation model called “WaterMet²” is presented. The DSS permits the user to identify one or more optimal Alternatives over a fixed long-term planning horizon using performance metrics mapped to the TRUST sustainability criteria (Alegre et al., 2012). The DSS exposes lists of in-built intervention options and system performance metrics for the user to compose new Alternatives. The quantitative metrics are calculated by the WaterMet² model and further qualitative or user-defined metrics may be specified by the user or by external tools feeding into the DSS. A Multi-Criteria Decision Analysis (MCDA) approach is employed within the DSS to compare the defined Alternatives and to rank them with respect to a pre-specified weighting scheme for different Scenarios. Two rich, interactive Graphical User Interfaces, one desktop and one web-based, are employed to assist with guiding the end user through the stages of defining the problem, evaluating and ranking Alternatives. This mechanism provides a useful tool for decision makers to compare different strategies for the planning of UWS with respect to multiple Scenarios. The efficacy of the DSS is demonstrated on a northern European case study inspired by a real-life urban water system for a mixture of quantitative and qualitative criteria. The results demonstrate how the DSS, integrated with an UWS modelling approach, can be used to assist planners in meeting their long-term, strategic level sustainability objectives

Review of trace toxic elements (Pb, Cd, Hg, As, Sb, Bi, Se, Te) and their deportment in gold processing. Part 1: Mineralogy, aqueous chemistry and toxicity

A literature review on the deportment of trace toxic elements (Pb, Cd, Hg, As, Sb, Bi, Se, and Te) in gold processing by cyanidation is presented which compiles the current knowledge in this area and highlights the gaps. This review, together with further research on the gaps in the thermodynamics and kinetics of these systems, aims to support the development of computer models to predict the chemical speciation and deportment of these elements through the various stages of the gold cyanidation process. The first part of this review is a collation of the relevant information on trace element mineralogy, aqueous chemistry and toxicity, together with a comparison of two available software packages (JESS and OLI) for thermodynamic modelling. Chemical speciation modelling can assist in understanding the chemistry of the trace toxic elements in gold cyanidation solutions which remains largely unexplored. Many significant differences exist between the predicted speciation of these trace elements for different types of modelling software due to differences in the thermodynamic data used, the paucity of data that exists under appropriate non-ideal conditions, and the methods used by the software packages to estimate thermodynamic parameters under these conditions. The toxicity and environmental guidelines of the chosen trace element species that exist in aqueous solutions are discussed to better understand the health and environmental risks associated with the presence of these elements in gold ores