Application of Non-dominated Sorting Genetic Algorithm in Calibration of HBV Rainfall-runoff Model: a Case Study of Tsengwen Reservoir Catchment in Southern Taiwan

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Methodological review of multicriteria optimization techniques: aplications in water resources

Multi-criteria decision analysis (MCDA) is an umbrella approach that has been applied to a wide range of natural resource management situations. This report has two purposes. First, it aims to provide an overview of advancedmulticriteriaapproaches, methods and tools. The review seeks to layout the nature of the models, their inherent strengths and limitations. Analysis of their applicability in supporting real-life decision-making processes is provided with relation to requirements imposed by organizationally decentralized and economically specific spatial and temporal frameworks. Models are categorized based on different classification schemes and are reviewed by describing their general characteristics, approaches, and fundamental properties. A necessity of careful structuring of decision problems is discussed regarding planning, staging and control aspects within broader agricultural context, and in water management in particular. A special emphasis is given to the importance of manipulating decision elements by means ofhierarchingand clustering. The review goes beyond traditionalMCDAtechniques; it describes new modelling approaches. The second purpose is to describe newMCDAparadigms aimed at addressing the inherent complexity of managing water ecosystems, particularly with respect to multiple criteria integrated with biophysical models,multistakeholders, and lack of information. Comments about, and critical analysis of, the limitations of traditional models are made to point out the need for, and propose a call to, a new way of thinking aboutMCDAas they are applied to water and natural resources management planning. These new perspectives do not undermine the value of traditional methods; rather they point to a shift in emphasis from methods for problem solving to methods for problem structuring. Literature review show successfully integrations of watershed management optimization models to efficiently screen a broad range of technical, economic, and policy management options within a watershed system framework and select the optimal combination of management strategies and associated water allocations for designing a sustainable watershed management plan at least cost. Papers show applications in watershed management model that integrates both natural and human elements of a watershed system including the management of ground and surface water sources, water treatment and distribution systems, human demands,wastewatertreatment and collection systems, water reuse facilities,nonpotablewater distribution infrastructure, aquifer storage and recharge facilities, storm water, and land use

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

Constrained shuffled complex evolution algorithm and its application in the automatic calibration of Xinanjiang model

The Shuffled Complex Evolution—University of Arizona (SCE-UA) is a classical algorithm in the field of hydrology and water resources, but it cannot solve constrained optimization problems directly. Using penalty functions has been the preferred method to handle constraints, but the appropriate selection of penalty parameters and penalty functions can be challenging. To enhance the universality of the SCE-UA, we propose the Constrained Shuffled Complex Evolution Algorithm (CSCE) to conveniently and effectively solve inequality-constrained optimization problems. Its performance is compared with the SCE-UA using the adaptive penalty function (SCEA) on 14 test problems with inequality constraints. It is further compared with seven other algorithms on two test problems with low success rates. To demonstrate its effect in hydrologic model calibration, the CSCE is applied to the parameter optimization of the Xinanjiang (XAJ) model under synthetic data and observed data. The results indicate that the CSCE is more advantageous than the SCEA in terms of the success rate, stability, feasible rate, and convergence speed. It can guarantee the feasibility of the solution and avoid the problem of deep soil tension water capacity (WDM)<0 in the optimization process of the XAJ model. In the case of synthetic data, the CSCE can accurately find the theoretical optimal parameters of the XAJ model under the given constraints. In the case of observed data, the XAJ model optimized by the CSCE can effectively simulate the hourly rainfall-runoff events of the Hexi Basin and achieves mean Nash efficiency coefficients greater than 0.75 in the calibration period and the validation period

Structural Best Management Practices (BMPs) and hydrological effects modelling using swat for urban watershed

Orientador: Prof. Dr. Cristovao V.S. FernandesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Recursos Hídricos e Ambiental. Defesa : Curitiba, 15/03/2019Inclui referências: p. 128-141Resumo: As Best Management Practices (BMPs) têm sido usadas como solução para mitigação de condições de pós-desenvolvimento em bacias urbanas e rurais. Estes dispositivos regulam vazões e volumes, além de capturar poluentes do escoamento superficial usando vários mecanismos. Estes dispositivos têm sido estudados e seu uso disseminado em vários países. Concomitantemente, o melhoramento de modelos de transporte e destinação de constituintes para investigar os efeitos, algoritmos para otimizar a busca por locais ótimos de instalação e facilitação da avaliação de entradas e saídas trouxe à luz vários desafios no que tange a modelagem dos fenômenos, incluindo a seleção de escalas de dimensão e tempo adequadas à representação dos fenômenos. A revisão de literatura demonstra uma fronteira clara entre usar inputs massivos de dados e computação exaustiva em modelos para descrição detalhada dos processos ou a adoção de abordagens mais simplificadas que capturem áreas maiores a custos menores de levantamento de dados. Neste estudo o Soil and Water Assessment Tool (SWAT) é utilizado como solução harmônica para modelagem em bacias com usos do solo mistos. Para vencer os desafios acima citados, BMPs são tratadas como zonas de recarga, isto é, zonas com Números de Curva (CN) menores. A localização destes dispositivos no modelo é realizada utilizando critérios consolidados de viabilidade através de ferramentas já desenvolvidas. Quatro cenários de redução percentual são utilizados para avaliação das melhoras de fluxo nas escalas da Hydrological Response Unit (HRU), subbacia e curso do rio(reach): 10%, 30%, 50% e 70%. As mudanças foram avaliadas na escala diária e anual, usando aplicações desenvolvidas em Python para automatizar a parametrização do modelo e a entrada e saída de dados. O estudo foi bem-sucedido em conceber a geração de múltiplos cenários, assim como em produzir ferramentas que auxiliem a entrada e saída de dados. Os resultados demonstram que a criação de zonas de recarga é mais eficaz em regiões onde há mais capacidade de retenção do solo. Do contrário, a redução do escoamento superficial tende a chegar em um limite, a partir do qual não há mais roteamento do escoamento superficial. Em HRUs e subbacias onde as condições de solo são favoráveis, a dinâmica de roteamento superficial e subsuperficial é modificada, fazendo com que a recarga dos aquíferos aumente e as recessões sejam mais lentas. Em geral, não são visíveis efeitos na escala da subbacia e no curso principal do rio, uma vez que muito do escoamento superficial é roteado como escoamento lateral ou fluo de subsuperfície. Além disso, a superposição dos efeitos para o resto da bacia é muito pequena na escala diária. Palavras-chave: SWAT. Bacias Urbanas. Python. Best Management Practices Hidrologia.Abstract: Best Management Practice (BMP) devices have been employed as a solution for both agricultural and urban watershed post-development effect mitigation. These devices regulate flow and capture runoff pollutants using various mechanisms. Such devices have been studied and its use disseminated in several countries. Concurrently, the enhancement of pollutant fate and transport models to assess the effects, search for optimal locations and facilitate inputs has brought to light several challenges concerning the modelling of physical phenomena, especially the one related to selecting time and size scales for adequate representation. The literature revision demonstrates that a clear boundary between using massive data inputs and computation-exhaustive models for thorough process description or more simplified approaches that capture larger areas at a more affordable data cost has limited the comprehension and description of BMP hydrological processes at the subbasin and watershed scale. In this study, SWAT is used a harmonic solution for modelling mixed land-use watersheds. To overcome the challenges stated, BMPs are treated as recharge - lower Curve Number (CN) zones, in feasible scenarios generated using an pre-built-tool and consolidated feasibility topographic, hydrological and space-distribution features. Four scenarios were generated: 10, 30, 50 70% CN reductions were tested and evaluated at the daily HRU/subbasin and subbasin yearly average scales, using developed applications for automating the parameter change and Input/output operations. The study was successful in automating the BMP scenario generation and multiple scenario generation as well as output data analysis. Results show that the creation of recharge zones is more effective at regions where more soil storage is available. Otherwise, runoff reduction tends to reach a limit. In HRUs and subbasins where soil conditions are favorable, the entire soil water and groundwater flow dynamics is modified, causing aquifer recharge to increase on average and recessions to be slower. Generally, no effects can be noticed at the subbasin o reach scale, as much of the runoff is also routed either as lateral flow or groundwater flow. The superposition of such effects to the rest of the watershed results in small differences at the daily scale. Keywords: SWAT. Urban watersheds. Python. Best Management Practices. Hydrology

Development of emergency response systems by intelligent and integrated approaches for marine oil spill accidents

Oil products play a pervasive role in modern society as one of the dominant energy fuel sources. Marine activities related to oil extraction and transportation play a vital role in resource supply. However, marine oil spills occur due to such human activities or harsh environmental factors. The emergency accidents of spills cause negative impacts on the marine environment, human health, and economic loss. The responses to marine oil spills, especially large-scale spills, are relatively challenging and inefficient due to changing environmental conditions, limited response resources, various unknown or uncertain factors and complex resource allocation processes. The development of previous research mainly focused on single process simulation, prediction, or optimization (e.g., oil trajectory, weathering, or cleanup optimization). There is still a lack of research on comprehensive and integrated emergency responses considering multiple types of simulations, types of resource allocations, stages of accident occurrence to response, and criteria for system optimizations. Optimization algorithms are an important part of system optimization and decision-making. Their performance directly affacts the quality of emergency response systems and operations. Thus, how to improve efficiency of emergency response systems becomes urgent and essential for marine oil spill management. The power and potential of integrating intelligent-based modeling of dynamic processes and system optimization have been recognized to better support oil spill responders with more efficient response decisions and planning tools. Meanwhile, response decision-making combined with human factor analysis can help quantitatively evaluate the impacts of multiple causal factors on the overall processes and operational performance after an accident. To address the challenges and gaps, this dissertation research focused on the development and improvement of new emergency response systems and their applications for marine oil spill response in the following aspects: 1) Realization of coupling dynamic simulation and system optimization for marine oil spill responses - The developed Simulation-Based Multi-Agent Particle Swarm Optimization (SA-PSO) modeling investigated the capacity of agent-based modeling on dynamic simulation of spill fate and response, particle swarm optimization on response allocation with minimal time and multi-agent system on information sharing. 2) Investigation of multi-type resource allocation under a complex simulation condition and improvement of optimization performance - The improved emergency response system was achieved by dynamic resource transportation, oil weathering and response simulations and resource allocation optimization. The enhanced particle swarm optimization (ME-PSO) algorithm performed outstanding convergence performance and low computation cost characteristics integrating multi-agent theory (MA) and evolutionary population dynamics (EPD). 3) Analysis and evaluation of influencing factors of multiple stages of spill accidents based on human factors/errors and multi-criteria decision making - The developed human factors analysis and classification system for marine oil spill accidents (HFACS-OS) framework qualitatively evaluated the influence of various factors and errors associated with the multiple operational stages considered for oil spill preparedness and response (e.g., oil spill occurrence, spill monitoring, decision making/contingency planning, and spill response). The framework was further coupled with quantitative data analysis by Fuzzy-based Technique for Order Preference by Similarity to Idea Solution (Fuzzy-TOPSIS) to enhance decision-making during response operations under multiple criteria. 4) Development of a multi-criteria emergency response system with the enhanced optimization algorithm, multi-mode resource transportation and allocation and a more complex and realistic simulation modelling - The developed multi-criteria emergency response system (MC-ERS) system integrated dynamic process simulations and weighted multi-criteria system optimization. Total response time, response cost and environmental impacts were regarded as multiple optimization goals. An improved weighted sum optimization function was developed to unify the scaling and proportion of different goals. A comparative PSO was also developed with various algorithm-improving methods and the best-performing inertia weight function. The proposed emergency response approaches in studies were examined by oil spill case studies related to the North Atlantic Ocean and Canada circumstances to analyze the modelling performance and evaluate their practicality and applicability. The developed optimization algorithms were tested by benchmarked functions, other optimization algorithms, and an oil spill case. The developed emergency response systems and the contained simulation and optimization algorithms showed the strong capability for decision-making and emergency responses by recommending optimal resource management or evaluations of essential factors. This research was expected to provide time-efficient, and cost-saving emergency response management approaches for handling and managing marine oil spills. The research also improved our knowledge of the significance of human factors/errors to oil spill accidents and response operations and provided improved support tools for decision making. The dissertation research helped fill some important gaps in emergency response research and management practice, especially in marine oil spill response, through an innovative integration of dynamic simulation, resource optimization, human factor analysis, and artificial intelligence methods. The research outcomes can also provide methodological support and valuable references for other fields that require timely and effective decisions, system optimizations, process controls, planning and designs under complicated conditions, uncertainties, and interactions

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools