共查询到20条相似文献，搜索用时 359 毫秒
1.
Multi-objective optimization for free-phase LNAPL recovery using evolutionary computation algorithms
Abstract A nonlinear, multi-objective optimization methodology is presented that seeks to maximize free product recovery of light non-aqueous phase liquids (LNAPLs) while minimizing operation cost, by introducing the novel concept of optimal alternating pumping and resting periods. This process allows more oil to flow towards the extraction wells, ensuring maximum free product removal at the end of the remediation period with minimum groundwater extraction. The methodology presented here combines FEHM (Finite Element Heat and Mass transfer code), a multiphase groundwater model that simulates LNAPL transport, with three evolutionary algorithms: the genetic algorithm (GA), the differential evolution (DE) algorithm and the particle swarm optimization (PSO) algorithm. The proposed optimal free-phase recovery strategy was tested using data from a field site, located near Athens, Greece. The PSO and DE solutions were very similar, while that provided by the GA was inferior, although the computation time was roughly the same for all algorithms. One of the most efficient algorithms (PSO) was chosen to approximate the optimal Pareto front, a method that provides multiple options to decision makers. When the optimal strategy is implemented, although a significant amount of LNAPL free product is captured, a spreading of the LNAPL plume occurs. Editor Z.W. Kundzewicz; Associate editor L. See Citation Dokou, Z. and Karatzas, G.P., 2013. Multi-objective optimization for free-phase LNAPL recovery using evolutionary computation algorithms. Hydrological Sciences Journal, 58 (3), 671–685. 相似文献
2.
用随机启发式优化算法反演2003年云南大姚地震断层面参数的比较研究 总被引：1，自引：0，他引：1
基于余震分布确定主震断层面的数学模型,以确定断层面的走向和倾角参数进行计算,研究了遗传算法、模拟退火算法、差分演化算法、粒子群算法等4种最优化反演方法的反演效果和可靠性。结果显示,在涉及到的反演参数较少和非线性不太严重时,4种方法都有较好的表现,差分演化算法、粒子群算法速度快,精度高,遗传算法速度较慢,精度较低,模拟退火由于缺乏并行机制,速度较慢,精度高于遗传算法。余震在求出的断层附近分布图直观地反映出4种方法的效果和可靠性。 相似文献
3.
With the availability of spatially distributed data, distributed hydrologic models are increasingly used for simulation of spatially varied hydrologic processes to understand and manage natural and human activities that affect watershed systems. Multi‐objective optimization methods have been applied to calibrate distributed hydrologic models using observed data from multiple sites. As the time consumed by running these complex models is increasing substantially, selecting efficient and effective multi‐objective optimization algorithms is becoming a nontrivial issue. In this study, we evaluated a multi‐algorithm, genetically adaptive multi‐objective method (AMALGAM) for multi‐site calibration of a distributed hydrologic model—Soil and Water Assessment Tool (SWAT), and compared its performance with two widely used evolutionary multi‐objective optimization (EMO) algorithms (i.e. Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non‐dominated Sorted Genetic Algorithm II (NSGA‐II)). In order to provide insights into each method's overall performance, these three methods were tested in four watersheds with various characteristics. The test results indicate that the AMALGAM can consistently provide competitive or superior results compared with the other two methods. The multi‐method search framework of AMALGAM, which can flexibly and adaptively utilize multiple optimization algorithms, makes it a promising tool for multi‐site calibration of the distributed SWAT. For practical use of AMALGAM, it is suggested to implement this method in multiple trials with relatively small number of model runs rather than run it once with long iterations. In addition, incorporating different multi‐objective optimization algorithms and multi‐mode search operators into AMALGAM deserves further research. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
4.
In this paper,we apply particle swarm optimization(PSO),an artificial intelligence technique,to velocity calibration in microseismic monitoring.We ran simulations with four 1-D layered velocity models and three different initial model ranges.The results using the basic PSO algorithm were reliable and accurate for simple models,but unsuccessful for complex models.We propose the staged shrinkage strategy(SSS) for the PSO algorithm.The SSS-PSO algorithm produced robust inversion results and had a fast convergence rate.We investigated the effects of PSO's velocity clamping factor in terms of the algorithm reliability and computational efficiency.The velocity clamping factor had little impact on the reliability and efficiency of basic PSO,whereas it had a large effect on the efficiency of SSS-PSO.Reassuringly,SSS-PSO exhibits marginal reliability fluctuations,which suggests that it can be confidently implemented. 相似文献
5.
Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins. 相似文献
6.
In this study, the calibration of subsurface batch and reactive-transport models involving complex biogeochemical processes was systematically evaluated. Two hypothetical nitrate biodegradation scenarios were developed and simulated in numerical experiments to evaluate the performance of three calibration search procedures: a multi-start non-linear regression algorithm (i.e. multi-start Levenberg–Marquardt), a global search heuristic (i.e. particle swarm optimization), and a hybrid algorithm that combines the particle swarm procedure with a regression-based “polishing” step. Graphical analysis of the selected calibration problems revealed heterogeneous regions of extreme parameter sensitivity and insensitivity along with abundant numbers of local minima. These characteristics hindered the performance of the multi-start non-linear regression technique, which was generally the least effective of the considered algorithms. In most cases, the global search and hybrid methods were capable of producing improved model fits at comparable computational expense. In other cases, the multi-start and hybrid calibration algorithms yielded comparable fitness values but markedly differing parameter estimates and associated uncertainty measures. 相似文献
7.
Keurfon Luu Mark Noble Alexandrine Gesret Philippe Thierry 《Geophysical Prospecting》2020,68(2):536-552
The main goal of this study is to assess the potential of evolutionary algorithms to solve highly non-linear and multi-modal tomography problems (such as first arrival traveltime tomography) and their abilities to estimate reliable uncertainties. Classical tomography methods apply derivative-based optimization algorithms that require the user to determine the value of several parameters (such as regularization level and initial model) prior to the inversion as they strongly affect the final inverted model. In addition, derivative-based methods only perform a local search dependent on the chosen starting model. Global optimization methods based on Markov chain Monte Carlo that thoroughly sample the model parameter space are theoretically insensitive to the initial model but turn out to be computationally expensive. Evolutionary algorithms are population-based global optimization methods and are thus intrinsically parallel, allowing these algorithms to fully handle available computer resources. We apply three evolutionary algorithms to solve a refraction traveltime tomography problem, namely the differential evolution, the competitive particle swarm optimization and the covariance matrix adaptation–evolution strategy. We apply these methodologies on a smoothed version of the Marmousi velocity model and compare their performances in terms of optimization and estimates of uncertainty. By performing scalability and statistical analysis over the results obtained with several runs, we assess the benefits and shortcomings of each algorithm. 相似文献
8.
最优化测井解释方法能充分利用各种测井资料及地质信息,可以有效地评价复杂岩性油气藏.优化算法的选择是最优化测井解释方法的关键,影响着测井解释结果的准确性.细菌觅食算法(BFA)是新兴的一种智能优化算法,具有较强的全局搜索能力,但在寻优后期收敛速度较慢.复合形算法(CM)局部搜索能力极强,将其与BFA算法相结合构成BFA-CM混合算法,既提高了搜索精度又提高了搜索效率.利用BFA-CM最优化测井解释方法对苏里格致密砂岩储层实际资料进行了处理,计算结果与岩心及薄片分析资料吻合得很好. 相似文献
9.
《国际泥沙研究》2022,37(5):601-618
Landslides are considered as one among many phenomena jeopardizing human beings as well as their constructions. To prevent this disastrous problem, researchers have used several approaches for landslide susceptibility modeling, for the purpose of preparing accurate maps marking landslide prone areas. Among the most frequently used approaches for landslide susceptibility mapping is the Artificial Neural Network (ANN) method. However, the effectiveness of ANN methods could be enhanced by using hybrid metaheuristic algorithms, which are scarcely applied in landslide mapping. In the current study, nine hybrid metaheuristic algorithms, genetic algorithm (GA)-ANN, evolutionary strategy (ES)-ANN, ant colony optimization (ACO)-ANN, particle swarm optimization (PSO)-ANN, biogeography based optimization (BBO)-ANN, gravitational search algorithm (GHA)-ANN, particle swarm optimization and gravitational search algorithm (PSOGSA)-ANN, grey wolves optimization (GWO)-ANN, and probability based incremental learning (PBIL)-ANN have been used to spatially predict landslide susceptibility in Algiers’ Sahel, Algeria. The modeling phase was done using a database of 78 landslides collected utilizing Google Earth images, field surveys, and six conditioning factors (lithology, elevation, slope, land cover, distance to stream, and distance to road). Initially, a gamma test was used to decrease the input variable numbers. Furthermore, the optimal inputs have been modeled by the mean of hybrid metaheuristic ANN techniques and their performance was assessed through seven statistical indicators. The comparative study proves the effectiveness of the co-evolutionary PSOGSA-ANN model, which yielded higher performance in predicting landslide susceptibility compared to the other models. Sensitivity analysis using the step-by-step technique was done afterward, which revealed that the distance to the stream is the most influential factor on landslide susceptibility, followed by the slope factor which ranked second. Lithology and the distance to road have demonstrated a moderate effect on landslide susceptibility. Based on these findings, an accurate map has been designed to help land-use managers and decision-makers to mitigate landslide hazards. 相似文献
10.
This study presents single‐objective and multi‐objective particle swarm optimization (PSO) algorithms for automatic calibration of Hydrologic Engineering Center‐ Hydrologic Modeling Systems rainfall‐runoff model of Tamar Sub‐basin of Gorganroud River Basin in north of Iran. Three flood events were used for calibration and one for verification. Four performance criteria (objective functions) were considered in multi‐objective calibration where different combinations of objective functions were examined. For comparison purposes, a fuzzy set‐based approach was used to determine the best compromise solutions from the Pareto fronts obtained by multi‐objective PSO. The candidate parameter sets determined from different single‐objective and multi‐objective calibration scenarios were tested against the fourth event in the verification stage, where the initial abstraction parameters were recalibrated. A step‐by‐step screening procedure was used in this stage while evaluating and comparing the candidate parameter sets, which resulted in a few promising sets that performed well with respect to at least three of four performance criteria. The promising sets were all from the multi‐objective calibration scenarios which revealed the outperformance of the multi‐objective calibration on the single‐objective one. However, the results indicated that an increase of the number of objective functions did not necessarily lead to a better performance as the results of bi‐objective function calibration with a proper combination of objective functions performed as satisfactorily as those of triple‐objective function calibration. This is important because handling multi‐objective optimization with an increased number of objective functions is challenging especially from a computational point of view. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
11.
煤矿井下矿井瞬变电磁法(MTEM)探测中,电磁场呈全空间分布,全空间瞬变电磁反演是复杂的非线性问题,目前反演计算中全空间响应主要由半空间响应乘以全空间响应系数来得到,导致反演结果中顶板和底板异常(或前方和后方异常)叠加在一起难以分离,造成分辨率下降.论文提出采用粒子群优化算法(PSO)进行全空间MTEM反演,通过理论分析,在常规的粒子群算法基础上提出了一种新的进化公式改进策略,提高了粒子群算法的寻优能力.基于全空间瞬变电磁场理论,编写了粒子群算法反演程序,进行全空间条件下五层含巷道的复杂模型的反演计算.结合某矿井巷道顶板、底板岩层及断层含水性的探测实例,对实测数据进行反演计算和解释,探测结果得到钻探证实.研究表明,改进的粒子群优化算法对理论模型和实际资料的反演拟合程度较高,实现了矿井顶板、底板视电阻率异常的分离,提高了全空间瞬变电磁勘探资料的解释精度和分辨率. 相似文献
12.
13.
A method to reduce the computational requirement while assessing uncertainty of complex hydrological models 总被引：1，自引：1，他引：0
P. Athira K. P. Sudheer 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(3):847-859
The quantification of uncertainty in the simulations from complex physically based distributed hydrologic models is important for developing reliable applications. The generalized likelihood uncertainty estimation method (GLUE) is one of the most commonly used methods in the field of hydrology. The GLUE helps reduce the parametric uncertainty by deriving the probability distribution function of parameters, and help analyze the uncertainty in model output. In the GLUE, the uncertainty of model output is analyzed through Monte Carlo simulations, which require large number of model runs. This induces high computational demand for the GLUE to characterize multi-dimensional parameter space, especially in the case of complex hydrologic models with large number of parameters. While there are a lot of variants of GLUE that derive the probability distribution of parameters, none of them have addressed the computational requirement in the analysis. A method to reduce such computational requirement for GLUE is proposed in this study. It is envisaged that conditional sampling, while generating ensembles for the GLUE, can help reduce the number of model simulations. The mutual relationship between the parameters was used for conditional sampling in this study. The method is illustrated using a case study of Soil and Water Assessment Tool (SWAT) model on a watershed in the USA. The number of simulations required for the uncertainty analysis was reduced by 90 % in the proposed method compared to existing methods. The proposed method also resulted in an uncertainty reduction in terms of reduced average band width and high containing ratio. 相似文献
14.
视震源时间函数的提取是研究震源参数的重要途径. 本文提出了利用改进的粒子群(PSO)算法反演视震源时间函数的方法, 以水平线方法得到的结果作为PSO算法的初值, 并对PSO算法的惯性因子和学习因子进行改进, 提高计算效率. 采用改进的PSO算法对模拟数据进行了反演计算, 并与映射Landweber反褶积(PLD)方法和遗传算法(GA)进行了对比分析. 结果表明, 相对于PLD方法, 改进的PSO算法反演结果与真实结果误差更小; 相对于遗传算法, 改进的PSO算法计算效率提高了5倍以上. 最后, 利用改进的算法对2005年10月8日巴基斯坦克什米尔MW7.6地震的P波视震源时间函数进行了提取, 结果表明此次地震P波视震源时间函数在25 s之内, 震源沿西北向破裂. 该结果与张勇等的结果一致. 相似文献
15.
With the recent development of distributed hydrological models, the use of multi‐site observed data to evaluate model performance is becoming more common. Distributed hydrological model have many advantages, and at the same time, it also faces the challenge to calibrate over‐do parameters. As a typical distributed hydrological model, problems also exist in Soil and Water Assessment Tool (SWAT) parameter calibration. In the paper, four different uncertainty approaches – Particle Swarm Optimization (PSO) techniques, Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting algorithm (SUFI‐2) and Parameter Solution (PARASOL) – are taken to a comparative study with the SWAT model applied in Peace River Basin, central Florida. In our study, the observed river discharge data used in SWAT model calibration were collected from the three gauging stations at the main tributary of the Peace River. Behind these approaches, there is a shared philosophy; all methods seek out many parameter set to fit the uncertainties due to the non‐uniqueness in model parameter evaluation. On the basis of the statistical results of four uncertainty methods, difficulty level of each method, the number of runs and theoretical basis, the reasons that affected the accuracy of simulation were analysed and compared. Furthermore, for the four uncertainty method with SWAT model in the study area, the pairwise correlation between parameters and the distributions of model fit summary statistics computed from the sampling over the behavioural parameter and the entire model calibration parameter feasible spaces were identified and examined. It provided additional insight into the relative identifiability of the four uncertainty methods Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
16.
Plume containment using pump-and-treat (PAT) technology continues to be a popular remediation technique for sites with extensive groundwater contamination. As such, optimization of PAT systems, where cost is minimized subject to various remediation constraints, is the focus of an important and growing body of research. While previous pump-and-treat optimization (PATO) studies have used discretized (finite element or finite difference) flow models, the present study examines the use of analytic element method (AEM) flow models. In a series of numerical experiments, two PATO problems adapted from the literature are optimized using a multi-algorithmic optimization software package coupled with an AEM flow model. The experiments apply several different optimization algorithms and explore the use of various pump-and-treat cost and constraint formulations. The results demonstrate that AEM models can be used to optimize the number, locations and pumping rates of wells in a pump-and-treat containment system. Furthermore, the results illustrate that a total outflux constraint placed along the plume boundary can be used to enforce plume containment. Such constraints are shown to be efficient and reliable alternatives to conventional particle tracking and gradient control techniques. Finally, the particle swarm optimization (PSO) technique is identified as an effective algorithm for solving pump-and-treat optimization problems. A parallel version of the PSO algorithm is shown to have linear speedup, suggesting that the algorithm is suitable for application to problems that are computationally demanding and involve large numbers of wells. 相似文献
17.
The curve number (CN) method is widely used for rainfall–runoff modelling in continuous hydrologic simulation models. A sound continuous soil moisture accounting procedure is necessary for models using the CN method. For shallow soils and soils with low storage, the existing methods have limitations in their ability to reproduce the observed runoff. Therefore, a simple one‐parameter model based on the Soil Conservation Society CN procedure is developed for use in continuous hydrologic simulation. The sensitivity of the model parameter to runoff predictions was also analysed. In addition, the behaviour of the procedure developed and the existing continuous soil moisture accounting procedure used in hydrologic models, in combination with Penman–Monteith and Hargreaves evapotranspiration (ET) methods was also analysed. The new CN methodology, its behaviour and the sensitivity of the depletion coefficient (model parameter) were tested in four United States Geological Survey defined eight‐digit watersheds in different water resources regions of the USA using the SWAT model. In addition to easy parameterization for calibration, the one‐parameter model developed performed adequately in predicting runoff. When tested for shallow soils, the parameter is found to be very sensitive to surface runoff and subsurface flow and less sensitive to ET. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
18.
Ertan Pek?en Türker Yas A. Yekta Kayman Co?kun Özkan 《Journal of Applied Geophysics》2011,75(2):305-318
Particle swarm optimization (PSO) is a global search method, which can be used for quantitative interpretation of self-potential data in geophysics. At the result of this process, parameters of a source model, e.g., the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and regional coefficients are estimated. This study investigates the results and interpretation of a detailed numerical data of some simple body responses, contaminated and field data. The method is applied to three field examples from Turkey and the results are compared with the previous works. The statistics of particle swarm optimization and the corresponding model parameters are analyzed with respect to the number of generation. We also present the oscillations of the model parameters at the vicinity of the low misfit area. Further, we show how the model parameters and absolute frequencies are related to the total number of PSO iterations. Gaussian noise shifts the low misfit area region from the correct parameter values proportional to the level of errors, which directly affects the result of the PSO method. These effects also give some ambiguity of the model parameters. However, the statistical analyses help to decrease these ambiguities in order to find the correct values. Thus, the findings suggest that PSO can be used for quantitative interpretation of self-potential data. 相似文献
19.
Advantages of the analytic element method for the solution of groundwater management problems 总被引：1，自引：0，他引：1
In the simulation‐optimization approach, a coupled optimization and groundwater flow/transport model is used to solve groundwater management problems. The efficiency of the numerical method, which is used to simulate the groundwater flow, is one the major reason to obtain the best solution for a management problem. This study was carried out to examine the advantages of the analytic element method (AEM) in the simulation‐optimization approach, for the solution of groundwater management problems. For this study, the AEM and finite difference method (FDM) based flow models were developed and coupled with the particle swarm optimization (PSO)‐based optimization model. Furthermore, the AEM‐PSO and FDM‐PSO models developed were applied in hypothetical as well as real field conditions to address groundwater management problems and the results were compared. For the real field situation, the models developed were applied to the Dore River basin in France to minimize the installation and operational cost of new pumping wells taking the location and discharge of the pumping wells as decision variables. The constraints of the problem were identified with the help of stakeholders and water authority officials. The AEM flow model was developed to facilitate the management model particularly when at each iteration, the optimization model calls for a simulation model to calculate the values of groundwater heads. The results show that, at some points, the AEM‐PSO model is efficient in identifying the optimal location of wells and consequently results in optimal costs, sometimes difficult when using the FDM. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
20.
This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R2 parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R2), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models. 相似文献