粒子群优化算法在确定越流含水层参数中的应用

以第1类越流系统含水层中的非稳定流井流问题的解析解为基础,将粒子群优化算法应用于分析抽水试验数据、解决确定含水层参数的问题中,并就学习因子c1,c2及惯性权重系数ω等算法控制参数对算法搜索能力的影响进行数值试验。结果表明:①粒子群优化算法能够有效地应用于分析抽水试验数据、确定第1类越流系统的含水层参数,且结果精度不易受人为影响;②c1,c2和ω综合影响粒子群的搜索能力,当c1=c2=2.0,ω由0.9随迭代次数线性递减至0.4时,有利于粒子在个体极值和全局极值周围几率均衡地搜索,并有利于保持粒子的搜索能力。     

自步学习在确定含水层参数中的应用

通过分析受到误差影响的抽水试验数据进行含水层估计,为含水层参数估计提供方法支持。以机器学习领域的自步学习方法为基础,构造了基于差分进化算法优化的自步学习方法,并将其应用到含水层参数确定中;在不同误差水平下,与其他方法进行对比试验。结果表明,在不同误差水平下,估计参数值与传统估计值之间以及仿真数据与原始数据之间均保持较小差异;基于抽水试验数据估计含水层参数的自步学习方法估计结果有效可靠,算法对误差的稳定容错性强。     

云神经网络确定含水层参数研究

高效、精确的含水层参数求解方法一直是水文地质研究领域的重要研究内容之一。实践中通常利用非稳定流抽水试验资料通过配线法确定含水层参数,但是随着计算机应用的普及,已有人开发出几种在非稳定流试验条件下求解含水层水文地质参数的快速、精确的计算机智能优化算法。在此基础上尝试建立了云神经网络模型(Cloud Neural Net,CNN),并将其应用于石家庄市元氏县3个单孔非稳定流抽水试验,对承压含水层参数进行计算,模型计算结果与当地的水文地质条件较为符合,且比传统方法及单纯的人工神经网络模型所得结果更加精确。因此云神经网络模型为研究区地下水资源评价、地下水数值模拟以及溶质运移模拟提供了另一种重要手段。     

飞蛾火焰优化算法在承压含水层参数反演中的应用

通过10个典型低维函数对一种新型群体智能仿生算法——飞蛾火焰优化(MFO)算法进行仿真验证,并与粒子群优化(PSO)算法的寻优结果进行对比。以无界井流问题及直线隔水边界附近井流问题的解析解为基础,将MFO算法应用于分析抽水试验数据,进行反演承压含水层参数,并以2个实例对MFO算法进行验证。结果表明:MFO算法在低维函数极值寻优问题上具有较好的收敛精度和全局寻优能力,寻优精度较PSO算法提高了7个数量级以上。MFO算法对2个实例的反演精度较文献改进SA算法等提高了56.5%以上,具有较好的稳健性能、收敛速度和全局寻优能力。利用MFO算法对承压含水层参数进行反演,可获得比相关文献更高的反演精度,不但为精确估计承压含水层参数提供了有效方法,而且拓展了MFO算法在地下水模型参数反演中的应用,具有良好的应用价值和前景。     

A New Inverse Solution Assessment for the Recovery Test Using Radial Basis Function Collocation Method

Recovery pumping tests are still one of the most commonly preferred preliminary design steps in the assessment of aquifer hydraulics. The use of the existing methods would be insufficient under non-ideal aquifer conditions (i.e. heterogeneity, measurement errors, and boundary effect, etc.) which violate the Theis model assumptions developed for recovery test analysis. In this study, a new parameter estimation methodology based on the Radial Basis Collocation Method (RBFCM) was formulated to access the hydraulic parameters in a reliable, robust and accurate manner using recovery pump test. The suggested approach was established on the idea that the dimensionless time value at the pump-cessation which serves as a matching value is obtained by means of RFBCM. The proposed approach is straightforward to implement; which requires no data refinement, additional parameter, and visual match. The performance of the proposed method was tested with several aquifer conditions including homogeneous synthetic data, heterogeneous aquifer simulation and real field applications. The results confirm that the proposed method has a parameter estimation capacity as high as the available techniques in the literature and provides the practitioners to understand a more accurate portrayal of the effects of heterogeneity on the hydraulic parameters during the test process. In addition, the suggested methodology for the interpretation of aquifer recovery test can be evaluated to be employed as a diagnostic tool to identify non-ideal conditions. The potential use of RBFCM in this study was also presented as the supplement of aquifer test interpretation assessment.     

改进粒子群算法在确定含水层参数中的应用

通过分析抽水试验数据,为估计含水层参数提供新的方法。在粒子多样性方面对粒子群算法进行改进,提高了算法的收敛速度和精度。将改进的粒子群优化算法应用到含水层参数估计中,计算结果与其他方法进行对比,并对不同初始值范围下参数估计值进行分析探讨。结果表明:改进粒子群算法估计结果相对误差(7.3%和4.5%)小于其他方法,且目标函数值相对更小,达到0.335×10~(-5);对于不同初始参数范围,利用此算法均能达到满意结果且寻优率高。基于抽水试验数据估计含水层参数的改进粒子群优化算法计算结果有效且可靠,算法收敛速度快,寻优能力强,稳定性好。     

Coastal Aquifer Management Based on the Joint use of Density-Dependent and Sharp Interface Models

In pumping optimization of coastal aquifers, the evaluation of the objective function and constraints using density-dependent models is overwhelmed by complex and time-consuming numerical simulations. To address those cases where the available density-dependent model runs are very limited, due to excessive computational burden, an efficient optimization strategy is developed. The proposed methodology uses an efficient sharp interface model jointly with a complex density-dependent model in an evolutionary optimization algorithm. While most evaluations are based on the sharp interface model, the density-dependent model is selectively called to evaluate promising solutions and to improve the predictions of the sharp interface model through the adaptive modification of the saltwater-freshwater density ratio. The method is tested for pumping optimization problems in confined and unconfined coastal aquifers with multiple pumping wells. The optimal solutions are compared to those obtained by density-dependent as well as by sharp interface optimization alone. Under a very restrictive computational budget, the best feasible solution is attained in less than 25 density-dependent model runs for two optimization problems of 10 and 20 decision variables. The results indicate that this optimization method leads to good feasible solutions and that an improved estimation of optimal pumping rates can be achieved within a limited computational budget. The method could also stand as an efficient preliminary exploration of the optimal search space, to provide good feasible starting points for the implementation of more comprehensive methods of coastal aquifer management.

     

确定越流含水层参数的混沌序列优化算法

将混沌序列优化算法应用在第一类越流系统含水层非稳定流井流问题上,进行抽水试验数据的分析、含水层参数的求解,并就算法的搜索能力和结果与给定含水层各参数取值范围的关系进行探讨。结果表明:(1)求解越流条件下含水层参数的计算问题能用混沌序列优化算法得到很好的解决;(2)除越流因数上限取值会降低越流因数搜索结果的准确性外,储水系数、导水系数、越流因数上限取值对算法的搜索能力和搜索结果没有太明显的影响。相较于其他方法,混沌序列优化算法易于编程、运算简单、运算结果不被人为因素干扰等特点更为突出。     

Distributed Simulation‐optimization Model for Conjunctive Use of Groundwater and Surface Water Under Environmental and Sustainability Restrictions

Evolving optimal management strategies are essential for the sustainable development of water resources. A coupled simulation-optimization model that links the simulation and optimization models internally through a response matrix approach is developed for the conjunctive use of groundwater and surface water in meeting irrigation water demand and municipal water supply, while ensuring groundwater sustainability and maintaining environmental flow in river. It incorporates the stream-aquifer interactions, and the aquifer response matrix is generated from a numerical groundwater model. The optimization model is solved by using MATLAB. The developed model has been applied to the Hormat-Golina valley alluvial stream-aquifer system, Ethiopia, and the optimal pumping schedules were obtained for the existing 43 wells under two different scenarios representing with and without restrictions on stream flow depletion, and satisfying the physical, operational and managerial constraints arising due to hydrological configuration, sustainability and ecological services. The study reveals that the total annual optimal pumping is reduced by 19.75?% due to restrictions on stream flow depletion. It is observed that the groundwater pumping from the aquifer has a significant effect on the stream flow depletion and the optimal conjunctive water use plays a great role in preventing groundwater depletion caused by the extensive pumping for various purposes. The groundwater contribution in optimal conjunctive water use is very high having a value of 92?% because of limited capacity of canal. The findings would be useful to the planners and decision makers for ensuring long-term water sustainability.

     

Evaluation of Analytical Methods to Study Aquifer Properties with Pumping Tests in Coastal Aquifers with Numerical Modelling (Motril-Salobreña Aquifer)

Two pumping tests were performed in the unconfined Motril-Salobreña detrital aquifer in a 250 m-deep well 300 m from the coastline containing both freshwater and saltwater. It is an artesian well as it is in the discharge zone of this coastal aquifer. The two observation wells where the drawdowns are measured record the influence of tidal fluctuations, and the well lithological columns reveal high vertical heterogeneity in the aquifer. The Theis and Cooper-Jacob approaches give average transmissivity (T) and storage coefficient (S) values of 1460 m²/d and 0.027, respectively. Other analytical solutions, modified to be more accurate in the boundary conditions found in coastal aquifers, provide similar T values to those found with the Theis and Cooper-Jacob methods, but give very different S values or could not estimate them. Numerical modelling in a synthetic model was applied to analyse the sensitivity of the Theis and Cooper-Jacob approaches to the usual boundary conditions in coastal aquifers. The T and S values calculated from the numerical modelling drawdowns indicate that the regional flow, variable pumping flows, and tidal effect produce an error of under 10 % compared to results obtained with classic methods. Fluids of different density (freshwater and saltwater) cause an error of 20 % in estimating T and of over 100 % in calculating S. The factor most affecting T and S results in the pumping test interpretation is vertical heterogeneity in sediments, which can produce errors of over 100 % in both parameters.     

Automatic Shifting Method for the Identification of Generalized Radial Flow Parameters by Water Cycle Optimization

The general radial flow (GRF) could successfully analyze the groundwater flow in a fractured medium which has generally a more complex mechanism due to the scale-dependent heterogeneity and dynamic processes for both individual fracture and fracture networks. A new optimization scheme, referred to as the automatic shifting method (ASM), was established in order to eradicate the subjectivity and some definite difficulties in classical graphical curve matching (GCM) for the determination flow parameters of GRF from in-situ pumping test data. The logic behind the ASM is similar to GCM but it simplifies and enhances the estimation process by optimizing newly introduced parameters (the horizontal and vertical shifts) together with the flow dimension parameter via Water Cycle Algorithm (WCA). The proposed ASM was tested with several hypothetical pumping test scenarios as well as a number of real field data. In addition, the capability of WCA was thoroughly compared with other competitive derivative-free, nature inspired population-based optimization algorithms by implementing a multi decision criteria analysis. The proposed ASM with WCA could achieve the outstanding estimation performance for the implemented analyses. In conclusion, ASM has a great potential to be modified for interpreting test data obtained from different groundwater models.

     

Development and Implementation of Support Vector Machine Regression Surrogate Models for Predicting Groundwater Pumping-Induced Saltwater Intrusion into Coastal Aquifers

Predicting the extent of saltwater intrusion (SWI) into coastal aquifers in response to changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMr), an innovative artificial intelligence-based machine learning algorithm for predicting salinity concentrations at selected monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purpose, the prediction results of SVMr are compared with well-established genetic programming (GP) based surrogate models. SVMr and GP models are trained and validated using identical sets of input (pumping) and output (salinity concentration) datasets. The trained and validated models are then used to predict salinity concentrations at specified monitoring wells in response to new pumping datasets. Prediction capabilities of the two learning machines are evaluated using different proficiency measures to ensure their practicality and generalisation ability. The performance evaluation results suggest that the prediction capability of SVMr is superior to GP models. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. This sensitivity analysis provides a subset of most influential pumping rates, which is used to construct new SVMr surrogate models with improved predictive capabilities. The improved prediction capability and the generalisation ability of the SVMr models together with the ability to improve the accuracy of prediction by refining the input set for training makes the use of proposed SVMr models more attractive. Prediction models with more accurate prediction capability makes it potentially very useful for designing large scale coastal aquifer management strategies.     

Coupled Sharp-interface and Density-dependent Model for Simultaneous Optimization of Production Well Locations and Pumping in Coastal Aquifer

The main management challenge in coastal aquifers is to prevent saltwater intrusion, ensuring ample freshwater supply. Saltwater intrusion happens due to unregulated pumping from production wells. Therefore, it is essential to have an effective management policy, which ensures the requisite amount of freshwater to be withdrawn from coastal aquifers without causing saltwater intrusion. A methodology for optimizing production well locations and maximizing pumping from production wells is presented to achieve these conflicting objectives. The location of production wells directly affects the amount of freshwater pumped out of the coastal aquifer. Simultaneous optimization of production well locations and pumping from the same is achieved by linking mathematical simulation models with the optimization algorithm. A new methodology using coupled sharp-interface and density-dependent simulation models is developed to find optimal well locations and optimize the amount of freshwater pumped from the coastal aquifer. The performance of the developed methodology is evaluated for saltwater intrusion in the coastal city of Puri, India. The performance evaluation results show the developed methodology's applicability for managing saltwater intrusion while maximizing freshwater pumping in coastal aquifers under constraints of well location.

     

Groundwater Model Calibration by Meta-Heuristic Algorithms

Groundwater models are computer models that simulate or predict aquifer conditions by using input data sets and hydraulic parameters. Commonly, hydraulic parameters are extracted by calibration, using observed and simulated aquifer conditions. The accuracy of calibration affects other modeling processes, especially the hydraulic head simulation. Meta-heuristic algorithms are good candidates to determine optimal/near-optimal parameters in groundwater models. In this paper, two meta-heuristic algorithms: (1) particle swarm optimization (PSO) and (2) pattern search (PS) are applied and compared in the Ghaen aquifer, by considering the sum of the squared deviation (SSD) between observed and simulated hydraulic heads and the sum of the absolute value of deviation (SAD) between observed and simulated hydraulic heads as the objective functions. Results show that obtained values of the objective function are enhanced significantly by using the PS algorithm. Accordingly, PS improves (decreases) the SSD and SAD by 0.20 and 2.36 percent, respectively, compared to results reported by using the PSO algorithm. Results also indicate that the proposed PS optimization tool is effective in the calibration of aquifer parameters.     

Sustainability of aquifers supporting irrigated agriculture: a case study of the High Plains aquifer in Kansas

ABSTRACT

The only means of moderating alarming depletion rates in many of the world’s major aquifers is to reduce pumping. We apply a new water-balance approach to assess the impact of pumping reductions in a portion of the High Plains aquifer in the United States. Although projected aquifer responses to pumping reductions vary, practically achievable reductions would have a large impact throughout the area. These findings demonstrate that modest pumping reductions could greatly enhance prospects for groundwater-supported irrigation in the High Plains aquifer and similar aquifers elsewhere.     

Surrogate-Based Stochastic Multiobjective Optimization for Coastal Aquifer Management under Parameter Uncertainty

Linked simulation-optimization (S/O) approaches have been extensively used as tools in coastal aquifer management. However, parameter uncertainties in seawater intrusion (SI) simulation models often undermine the reliability of the derived solutions. In this study, a stochastic S/O framework is presented and applied to a real-world case of the Longkou coastal aquifer in China. The three conflicting objectives of maximizing the total pumping rate, minimizing the total injection rate, and minimizing the solute mass increase are considered in the optimization model. The uncertain parameters are contained in both the constraints and the objective functions. A multiple realization approach is utilized to address the uncertainty in the model parameters, and a new multiobjective evolutionary algorithm (EN-NSGA2) is proposed to solve the optimization model. EN-NSGA2 overcomes some inherent limitations in the traditional nondominated sorting genetic algorithm-II (NSGA-II) by introducing information entropy theory. The comparison results indicate that EN-NSGA2 can effectively ameliorate the diversity in Pareto-optimal solutions. For the computational challenge in the stochastic S/O process, a surrogate model based on the multigene genetic programming (MGGP) method is developed to substitute for the numerical simulation model. The results show that the MGGP surrogate model can tremendously reduce the computational burden while ensuring an acceptable level of accuracy.

     

Numerical simulation of groundwater flow and land deformation due to groundwater pumping in cross-anisotropic layered aquifer system

In a natural aquifer, the aquifer properties are complicated, which normally vary with aquifer depth and geographic characteristics. Most previous studies have been limited to the case of isotropic aquifer with uniform characteristics. In the proposed model, the fully coupled ground water flow and land deformation due to groundwater pumping in cross-anisotropic aquifer system is used to analyze and predict. Based on this model, pumping-recovery tests in various conditions are numerically simulated to reveal the effects of cross-anisotropic aquifer behavior on hydraulic head and land deformation. Finally, the proposed method is applied to a project in Shanghai of China due to pumping of groundwater predicted. Comparing the calculated results with the measured field values indicates that the methods can be a useful tool for designing groundwater pumping projects.     

A New Groundwater Management Model by Coupling Analytic Element Method and Reverse Particle Tracking with Cat Swarm Optimization

The performance of groundwater management models mostly depends upon the methodology employed to simulate flow and transport processes and the efficiency of optimization algorithms. The present study examines the effectiveness of cat swarm optimization (CSO) for groundwater management problems, by coupling it with the analytic element method (AEM) and reverse particle tracking (RPT). In this study, we propose two coupled simulation-optimization models, viz. AEM-CSO and AEM-RPT-CSO by combining AEM with RPT and CSO. Both the models utilize the added advantages of AEM, such as precise estimation of hydraulic head at pumping location and generation of continuous velocity throughout the domain. The AEM-CSO model is applied to a hypothetical unconfined aquifer considering two different objectives, i.e., maximization of the total pumping of groundwater from the aquifer and minimization of the total pumping costs. The model performance reflects the superiority of CSO in comparison with other optimization algorithms. Further, the AEM-RPT-CSO model is successfully applied to a hypothetical confined aquifer to minimize the total number of contaminant sources, within the time related capture zone of the wells, while maintaining the required water demand. In this model, RPT gets continuous velocity information directly from the AEM model. The performance evaluation of the proposed methodology, illustrates its ability to solve groundwater management problems.     

Analysis of pumping tests data from mutually leaky aquifers

Abstract:

Pumping tests in aquifers separated by semipervious layer are generally analyzed by using solutions for leafy aquifers that assume constant head distribution in the unpumped source bed. This may not be true under prolonged pumping. Hantush (1967) solutions take into account head decline in the unpumped source bed. The objective of this paper is to present procedures for pumping test analysis using some of these solutions. Both type curve matching and straight‐line methods have been presented. Procedures described in this paper can be used to analyze early‐time drawdown data of the pumped aquifer and the late late‐time drawdown data of the pumped and unpumped aquifer above the semipervious layer. The hydraulic properties for the pumped and unpumped aquifer and the semipervious layer estimated by different methods are quite comparable.     

微透水岩体单孔抽水试验渗透系数的分析——以西非某水电站为例

抽水试验成本较高,耗时长,而单孔抽水试验获取的试验数据有限,且紊流、井损等因素对水位影响较大,从而影响参数计算精度;同时,实际抽水试验常在多种复杂边界条件下进行,如试验孔有临河时,需结合多种含水层模型,使实际抽水试验条件尽可能与理论模型条件相近,选择合适的模型计算,相互比较验证,求取岩土体渗透系数;将计算成果与有其他水文地质试验结果比较,综合确定渗透系数,进一步提高了岩土体渗透系数的合理性。