Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method

Groundwater management involves conflicting objectives as maximization of discharge contradicts the criteria of minimum pumping cost and minimum piping cost. In addition, available data contains uncertainties such as market fluctuations, variations in water levels of wells and variations of ground water policies. A fuzzy model is to be evolved to tackle the uncertainties, and a multiobjective optimization is to be conducted to simultaneously satisfy the contradicting objectives. Towards this end, a multiobjective fuzzy optimization model is evolved. To get at the upper and lower bounds of the individual objectives, particle Swarm optimization (PSO) is adopted. The analytic element method (AEM) is employed to obtain the operating potentio metric head. In this study, a multiobjective fuzzy optimization model considering three conflicting objectives is developed using PSO and AEM methods for obtaining a sustainable groundwater management policy. The developed model is applied to a case study, and it is demonstrated that the compromise solution satisfies all the objectives with adequate levels of satisfaction. Sensitivity analysis is carried out by varying the parameters, and it is shown that the effect of any such variation is quite significant. Copyright © 2015 John Wiley & Sons, Ltd.     

Reproducing fling-step and forward directivity at near source site using of multi-objective particle swarm optimization and multi taper

This paper presents a technique to reproduce compatible seismogran3s involving permanent displacen3ent effects at sites close to the fault source. A multi-objective evolutionary algorithm is used to minimize the differences between the response spectra and multi-tapered power spectral delsilies corresponding to the recorded and simulated wavelbrms. The multi-taper method is used to reduce the spectral leakage that is inherent in the Fourier rams formed form ofwavelbrms, ieading to a reduction of variance in power spectral amplitudes, thus permitting the calibration of the two sets of data. The technique is implemented using the 1998-Fandoqa （lran） earthquake data and the results are compared with the actual observed data. Additionally, a comparison is made with a SAR interfcrometry study leading to fair agreement with the reported dislocation along the main fault. The simulation procedure and results are discussed and assessed concluding that, although the technique may be associated with uncertainties, it can still be used to reproduce wavelbnns at near source sites that include permanent dislocation, and can be used for seismic pertbrmance evaluation of structures in the region under study.     

Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm

Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different d...     

基于粒子群优化算法的叠前角道集子波反演

本文探讨了粒子群优化(PSO)算法在叠前地震角道集子波反演中的应用.在基本最优PSO算法的基础上,提出了对粒子更新速度进行平滑滤波的改进最优粒子群算法.由于代表子波的粒子的维数较大,如果粒子的各维元素相互独立,将导致粒子速度更新紊乱,影响搜索速度.通过对粒子速度进行三点均值滤波,加强了单个粒子各维元素的相互联系,并防止了粒子速度逃逸,使粒子更快地向有利于最优解的位置收敛.该方法应用于叠前角道集子波的反演中,取得了较好的子波反演效果,证明了本文方法的有效性.     

An inexact stochastic optimization model for agricultural irrigation management with a case study in China

In this study, an inexact stochastic optimization model (ITSOM) is developed for agricultural irrigation management with a case study in China. Functional intervals are introduced into the modeling framework to much accurately address the spatial and temporal variation of system components. According to the results of case study, the developed model shows effectiveness in dealing with functional information of system parameters, and brings no difficulty in obtaining optimal water allocation patterns. It is indicated that the surface water resource (i.e. Heshui River) has better be used as the major source, and proper exploration of groundwater can curtail the related expense and further increase the system net benefit. Among eight farms, hybrid rice farm is going to obtain the greatest amount of water than the others, while watermelon farm has the priority to get water due to its highest benefit and penalty rate. In comparison, water allocations to rapeseed and tea farms are to be minimal within the respective fluctuation ranges. Scenario analysis is also conducted to clarify the differences between ITSOM and a conventional interval two-stage stochastic programming (ITSP) model. A total of 60 scenarios are initiated respectively linking to 60 monthly ITSP models for the entire planning horizon. The results show that the optimal objective function values of all ITSP models always fall into the range of that obtained from ITSOM. As each ITSP solution can only correspond to the system condition under a certain time point, it is highly vulnerable to system variation.     

Reflection-constrained 2D and 3D non-hyperbolic moveout analysis using particle swarm optimization

The objective of moveout parameter inversion is to derive sets of parameter models that can be used for moveout correction and stacking at each common midpoint location to increase the signal-to-noise ratio of the data and to provide insights into the kinematic characteristics of the data amongst other things. In this paper, we introduce a data-driven user-constrained optimization scheme that utilizes manual picks at a point on each reflector within a common midpoint gather to constrain the search space in which an optimization procedure can search for the optimal parameter sets at each reflection. The picks are used to create boundary curves which can be derived approximately via an optimization technique or analytically via the derivation of an analytical bounds function. In this paper, we derive analytical forms of bounds functions for four different moveout cases. These are normal moveout, non-hyperbolic moveout, azimuthally dependent normal moveout and azimuthally dependent non-hyperbolic moveout. The optimization procedure utilized here to search for the optimal moveout parameters is the particle swarm optimization technique. However, any metaheuristic optimization procedure could be modified to account for the constraints introduced in this paper. The technique is tested on two-layer synthetic models based on three of the four moveout cases discussed in this paper. It is also applied to an elastic forward modelled synthetic model called the HESS model, and finally to real 2D land data from Alaska. The resultant stacks show a marked improvement in the signal-to-noise ratio compared to the raw stacks. The results for the normal moveout, non-hyperbolic moveout and azimuthally dependent normal moveout tests suggest that the method is viable for said models. Results demonstrate that our method offers potential as an alternative to conventional parameter picking and inversion schemes, particularly for some cases where the number of parameters in the moveout approximation is 2 or greater.     

Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization

Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed‐forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network‐based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method‐based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability.     

基于粒子群算法的俯冲带三维有效弹性厚度反演

岩石圈有效弹性厚度是表征岩石圈力学性质的参数,其反映了岩石圈挠曲变形的特征.本文在传统二维挠曲模型的基础上,提出了适用于俯冲及碰撞带的三维薄板挠曲模型.并发展了基于粒子群算法的俯冲带三维有效弹性厚度反演方法.该方法适用于挠曲参数存在横向差异的俯冲-碰撞带.最后利用该方法反演了马尼拉海沟处岩石圈的有效弹性厚度,结果显示：南海中央海盆岩石圈的有效弹性厚度随着距洋中脊距离的增加而增大;马尼拉海沟轴部弯矩在洋中脊两侧呈分段性变化,这表明南海俯冲板片在深部撕裂可能对浅部的挠曲形态产生影响.

     

基于混沌-粒子群优化的磁法数据非线性反演方法

磁法反演属于非线性最优化问题,具有多变量、目标函数多极值、反演多解性等特点,因此,需要稳定的和高效的优化反演算法.粒子群优化已开始被用于地球物理反演计算,但是对于高维数、多峰值函数,粒子群的收敛精度不高,容易陷入局部极值.如果将混沌局部搜索和粒子群优化的优势相结合,通过将种群搜索过程对应为混沌轨道的遍历过程,可使标准粒子群优化的搜索过程具有避免陷入局部极小的能力.本文利用混沌-粒子群优化用于磁法反演计算.数据试验结果表明,该方法可以用于磁法数据的地球物理非线性反演,并且在一定程度上优于标准粒子群优化方法.     

N2O emissions from regional agricultural lands--A case study of Guizhou Province,southwestern China

To preliminarily study N2O emissions and the importance of environmental parameters on N2O flux from subtropical agroecosystem in China,N2O flux measurements were made at three cultivated agricultural lands in Guizhou Province,southwestern China.Based on the test and validation of daily N2O flux and its several associated variables between DNDC model and field measurements,DNDC model has been employed to estimate total N2O emissions from entire agricultural lands and its spatial distribution at county scale in Guizhou in 1995,and to assess the contributions of cropping practices on N2O emissions.     

Improving sustainability of urban drainage systems for climate change adaptation using best management practices: a case study of Tehran,Iran

Although the effectiveness of best management practices (BMPs) in reducing urban flooding is widely recognized, the improved sustainability achieved by implementing BMPs in upstream suburban areas, reducing downstream urban floods, is still debated. This study introduces a new definition of urban drainage system (UDS) sustainability, focusing on BMP usage to enhance system performance after adaptation to climate change. Three types of hydraulic reliability index (HRI) plus robustness and improvability indices were used to quantify the potential enhanced sustainability of the system in a changing climate, together with a climate change adaptability index (CCAI). The sustainability of UDS for the safe conveyance of storm-water runoff was investigated under different land-use scenarios: No BMP, BMP in urban areas, and BMP inside and upstream of urban areas, considering climate change impacts. Rainfall–runoff simulation alongside drainage network modelling was conducted using a storm-water management model (US EPA SWMM) to determine the inundation areas for both base-line and future climatic conditions. A new method for disaggregating daily rainfall to hourly, proposed to provide a finer resolution of input rainfall to SWMM, was applied to a semi-urbanized catchment whose upstream runoff from mountainous areas may contribute to the storm-water runoff in downstream urban parts. Our findings confirm an increase in the number of inundation points and reduction in sustainability indices of UDS due to climate change. The results present an increase in UDS reliability from 4% to 16% and improvements in other sustainability indicators using BMPs in upstream suburban areas compared to implementing them in urban areas.     

湖泊富营养化响应与流域优化调控决策的模型研究进展

湖泊富营养化是全球水环境领域面临的长期挑战,富营养化响应与流域优化决策模型是制定经济和高效调控方案的关键.然而已有的模型研究综述主要集中于模型开发、案例应用、敏感性分析、不确定性分析等单一方面,而缺少针对非线性响应、生态系统长期演变等最新湖泊治理挑战的研究总结.本文对数据驱动的统计模型、因果驱动的机理模型和决策导向的优化模型进行了综述.其中,统计模型包含经典统计、贝叶斯统计和机器学习模型,常用于建立响应关系、时间序列特征分析以及预报预警;机理模型包含流域的水文与污染物输移模拟以及湖泊的水文、水动力、水质、水生态等过程的模拟,用于不同时空尺度的变化过程模拟,其中复杂机理模型的敏感性分析、参数校验、模型不确定性等需要较高的计算成本;优化模型结合机理模型形成“模拟优化”体系,在不确定性条件下衍生出随机、区间优化等多种方法,通过并行计算、简化与替代模型可一定程度上解决计算时间成本的瓶颈.本文识别了湖泊治理面临的挑战,包括:①如何定量表征外源输入的非线性叠加和湖泊氮、磷、藻变化的非均匀性?②如何提高优化调控决策和水质目标的关联与精准性?③如何揭示湖泊生态系统的长期变化轨迹与驱动因素?最后,本文针对这些挑战提出研究展望,主要包括:①基于多源数据融合与机器学习算法以提升湖泊的短期水质预测精度;②以生物量为基础的机理模型与行为驱动的个体模型的升尺度或降尺度耦合以表达多种尺度的物质交互过程;③机器学习算法与机理模型的直接耦合或数据同化以降低模拟误差;④时空尺度各异的多介质模拟模型融合以实现精准和动态的优化调控.     

An adaptive multilevel correlation analysis: a new algorithm and case study

ABSTRACT

An adaptive multilevel correlation analysis, a kind of data-driven methodology, is proposed. The analysis is done by subdividing the time series into segments such that adjacent segments have significantly different mean values. It is shown that the proposed methodology can provide multilevel information about the correlation between two variables. An integrated coefficient with its significance testing is also proposed to summarize the correlation at each level. Using the adaptive multilevel correlation analysis methodology, the correlation between streamflow and water level is investigated for a case study, and the results indicate that real correlation might be far more complicated than the empirically constructed picture.

EDITOR D. Koutsoyiannis ASSOCIATE EDITOR E. Volpi     

The incorporation of stochasticity in risk analysis and management: a case study

 The selection of optimal management strategies for environmental contaminants requires detailed information on the risks imposed on populations. These risks are characterized by both inter-subject variability (different individuals having different levels of risk) and by uncertainty (there is uncertainty about the risk associated with the Yth percentile of the variability distribution). In addition, there is uncertainty introduced by the inability to agree fully on the appropriate decision criteria. This paper presents a methodology for incorporating uncertainty and variability into a multi-medium, multi-pathway, multi-contaminant risk assessment, and for placing this assessment into an optimization framework to identify optimal management strategies. The framework is applied to a case study of a sludge management system proposed for North Carolina and the impact of stochasticity on selection of an optimal strategy considered. Different sets of decision criteria reflecting different ways of treating stochasticity are shown to lead to different selections of optimal management strategies.     

Modelling particle residence times in agricultural river basins using a sediment budget model and fallout radionuclide tracers

Contemporary patterns in river basin sediment dynamics have been widely investigated but the timescales associated with current sediment delivery processes have received much less attention. Furthermore, no studies have quantified the effect of recent land use change on the residence or travel times of sediment transported through river basins. Such information is crucial for understanding contemporary river basin function and responses to natural and anthropogenic disturbances or management interventions. To address this need, we adopt a process‐based modelling approach to quantify changes in spatial patterns and residence times of suspended sediment in response to recent agricultural land cover change. The sediment budget model SedNet was coupled with a mass balance model of particle residence times based on atmospheric and fluvial fluxes of three fallout radionuclide tracers (⁷Be, excess ²¹⁰Pb and ¹³⁷Cs). Mean annual fluxes of suspended sediment were simulated in seven river basins (38–920 km²) in south‐west England for three land cover surveys (1990, 2000 and 2007). Suspended sediment flux increased across the basins from 0.5–15 to 1.4–37 kt y^‐1 in response to increasing arable land area between consecutive surveys. The residence time model divided basins into slow (upper surface soil) and rapid (river channel and connected hillslope sediment source area) transport compartments. Estimated theoretical residence times in the slow compartment decreased from 13–48 to 5.6–14 ky with the increase in basin sediment exports. In contrast, the short residence times for the rapid compartment increased from 185–256 to 260–368 d as the modelled connected source area expanded with increasing sediment supply from more arable land. The increase in sediment residence time was considered to correspond to longer sediment travel distances linked to larger connected source areas. This novel coupled modelling approach provides unique insight into river basin responses to recent environmental change not otherwise available from conventional measurement techniques. © 2014 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Flood management through LANDSAT TM and ERS SAR data: a case study

This paper deals with the experimental activity carried out in the field of flood monitoring at the Civil Engineering Department of Florence University (Italy) in cooperation with EOSAT (USA) and Eurimage (Italy). The aim of the study is to research the possibilities of satellite data utilization to aid in modelling of the hydrological behaviour of a river basin and monitoring flood emergencies. The area selected for the study is the Fucecchio Marsh (Tuscany, Italy), in which flooding events are very frequent. This paper describes the results of the study, with particular reference to the use of Landsat TM data to estimate soil water content, and the use of ERS SAR data to analyse flash flood events. © 1997 John Wiley & Sons, Ltd.     

A slow mode wave as a possible source of Pi 2 and associated particle precipitation: a case study

An intensification of auroral luminosity referred to as an auroral break-up often accompanies the onset of geomagnetic pulsation (Pi 2) at the dip-equator. One such auroral break-up occurred at 2239 UT on 16 June, 1986, being accompanied by weak substorm activity (AE≈50 nT) which was recorded in all-sky image of Syowa Station, Antarctica (66.2°S, 71.8°E in geomagnetic coordinates). The associated Pi 2 magnetic pulsation was detected by a fluxgate magnetometer in the afternoon sector at the dip-equator (Huancayo, Peru; 1.44°N, 355.9° in geomagnetic coordinates; 12.1°S, 75.2°W in geographic coordinates; L = 1.00). In spite of the large separation of the two stations in longitude and latitude, the auroral break-up and subsequent luminosity modulation were seen to be correlated with the wave form of the ground Pi 2 pulsation. This occurred in such a way that the luminosity maximum was seen to occur at the phase of maximum amplitudes of Pi 2 wave form. We argue that the observed correlation could be interpreted as indicating a Pi 2-modulation of a field-aligned acceleration of the low energy electrons that may occur near the equator of the midnight magnetosphere.     

Joint parameter estimation from magnetic resonance and vertical electric soundings using a multi‐objective genetic algorithm

Magnetic resonance sounding (MRS) has increasingly become an important method in hydrogeophysics because it allows for estimations of essential hydraulic properties such as porosity and hydraulic conductivity. A resistivity model is required for magnetic resonance sounding modelling and inversion. Therefore, joint interpretation or inversion is favourable to reduce the ambiguities that arise in separate magnetic resonance sounding and vertical electrical sounding (VES) inversions. A new method is suggested for the joint inversion of magnetic resonance sounding and vertical electrical sounding data. A one‐dimensional blocky model with varying layer thicknesses is used for the subsurface discretization. Instead of conventional derivative‐based inversion schemes that are strongly dependent on initial models, a global multi‐objective optimization scheme (a genetic algorithm [GA] in this case) is preferred to examine a set of possible solutions in a predefined search space. Multi‐objective joint optimization avoids the domination of one objective over the other without applying a weighting scheme. The outcome is a group of non‐dominated optimal solutions referred to as the Pareto‐optimal set. Tests conducted using synthetic data show that the multi‐objective joint optimization approximates the joint model parameters within the experimental error level and illustrates the range of trade‐off solutions, which is useful for understanding the consistency and conflicts between two models and objectives. Overall, the Levenberg‐Marquardt inversion of field data measured during a survey on a North Sea island presents similar solutions. However, the multi‐objective genetic algorithm method presents an efficient method for exploring the search space by producing a set of non‐dominated solutions. Borehole data were used to provide a verification of the inversion outcomes and indicate that the suggested genetic algorithm method is complementary for derivative‐based inversions.     

Sensitivity study using a genetic algorithm: inversion of amplitude variations with slowness

A sensitivity study of elastic parameters in amplitude-variation-with-slowness (AVS) for small- and large-offset seismic data is presented. In order to handle the non-linearity associated with waveform or amplitude beyond the critical slowness, an inversion algorithm based on Bayes' theory is used. A genetic algorithm was used to obtain the a posteriori probability density (PPD) function. The sensitivity analysis is performed on synthetic data containing P-wave as well as converted S-wave reflections. Four different two-layer models, which represent the typical range of AVS responses associated with the gas-sands normally encountered in exploration, were used to examine how well the elastic parameters can be inverted for different parametrizations by comparing the PPD functions. The sensitivity study results suggest that including wide-angle data in the inversion can greatly enhance the quality of inversion. The converted S-wave reflections can provide valuable extra information that can be used to extract elastic parameters. The results with noisy data demonstrate that the contrast of density and three velocity ratios can be estimated robustly with wide-angle reflection data.     

Improved microseismic event location by inclusion of a priori dip particle motion: a case study from Ekofisk

Microseismic monitoring in petroleum settings provides insights into induced and naturally occurring stress changes. Such data are commonly acquired using an array of sensors in a borehole, providing measures of arrival times and polarizations. Events are located using 1D velocity models, P‐ and S‐wave arrival times and the azimuths of P‐wave particle motions. However in the case of all the sensors being deployed in a vertical or near‐vertical borehole, such analysis leads to an inherent 180° ambiguity in the source location. Here we present a location procedure that removes this ambiguity by using the dip of the particle motion as an a priori information to constrain the initial source location. The new procedure is demonstrated with a dataset acquired during hydraulic fracture stimulation, where we know which side of the monitoring well the events are located. Using a 5‐step location procedure, we then reinvestigate a microseismic data set acquired in April 1997 at the Ekofisk oilfield in the North Sea. Traveltimes for 2683 candidate events are manually picked. A noise‐weighted analytic‐signal polarization analysis is used to estimate the dip and azimuth of P‐wave particle motions. A modified t‐test is used to statistically assess the reliability of event location. As a result, 1462 events are located but 627 are deemed to be statistically reliable. The application of a hierarchal cluster analysis highlights coherent structures that cluster around wells and inferred faults. Most events cluster at a depth of roughly 3km in the Ekofisk chalk formation but very little seismicity is observed from the underlying Tor chalk formation, which is separated from the Ekofisk formation by an impermeable layer. We see no evidence for seismicity in the overburden but such events may be too distant to detect. The resulting picture of microseismicity at Ekofisk is very different from those presented in previous studies.