Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selection

The issue of selecting appropriate model input parameters is addressed using a peak and low flow criterion (PLC). The optimal artificial neural network (ANN) models selected using the PLC significantly outperform those identified with the classical root‐mean‐square error (RMSE) or the conventional Nash–Sutcliffe coefficient (NSC) statistics. The comparative forecast results indicate that the PLC can help to design an appropriate ANN model to improve extreme hydrologic events (peak and low flow) forecast accuracy. Copyright © 2001 John Wiley & Sons, Ltd.     

Short‐term inflow forecasting using an artificial neural network model

The primary objective of this study is to investigate the possibility of including more temporal and spatial information on short‐term inflow forecasting, which is not easily attained in the traditional time‐series models or conceptual hydrological models. In order to achieve this objective, an artificial neural network (ANN) model for short‐term inflow forecasting is developed and several issues associated with the use of an ANN model are examined in this study. The formulated ANN model is used to forecast 1‐ to 7‐h ahead inflows into a hydropower reservoir. The root‐mean‐squared error (RMSE), the Nash–Sutcliffe coefficient (NSC), the A information criterion (AIC), B information criterion (BIC) of the 1‐ to 7‐h ahead forecasts, and the cross‐correlation coefficient between the forecast and observed inflows are estimated. Model performance is analysed and some quantitative analysis is presented. The results obtained are satisfactory. Perceived strengths of the ANN model are the capability for representing complex and non‐linear relationships as well as being able to include more information in the model easily. Although the results obtained may not be universal, they are expected to reveal some possible problems in ANN models and provide some helpful insights in the development and application of ANN models in the field of hydrology and water resources. Copyright © 2002 John Wiley & Sons, Ltd.     

Modelling evaporation using an artificial neural network algorithm

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.     

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.     

量子粒子群模糊神经网络碳酸盐岩流体识别方法研究

根据不同流体性质在角度道集上所反映特征的差异,构建了多属性角度叠加数据体组合流体识别因子.并将量子粒子群与模糊神经网络相结合,利用量子粒子群方法来优化模糊神经网络中的连接权值和隶属函数参数,并进行一系列的改进措施,显著提高了算法的全局寻优能力.将近远角度叠加数据体组合流体识别因子作为改进模糊神经网络的输入,流体性质作为输出,同时引入“相控流体识别”的思想,利用碳酸盐岩储集相进行控制,建立了碳酸盐岩流体识别模型.通过塔中实际井区进行验证,证明该方法能够提高流体的识别精度,具有很好的实际应用价值.     

基于改进粒子群算法的地震标量波方程反演

针对标准粒子群优化(PSO)算法存在易出现早熟而陷入局部最优以及进化后期收敛速度慢等缺陷,通过考虑粒子所处位置间相互作用,提出了一种改进的并行粒子群优化算法.由于引入粒子位置间的相互影响,减少了粒子搜索过程盲目性,因此能有效提高算法的收敛速度.数值试验表明,这种改进的粒子群算法适用于二维标量波方程的速度反演,且算法具有对初始模型依赖性低、收敛速度快、反演结果稳定、抗噪能力强等特点,为进一步将该反演算法用于弹性波波动方程以及弹性参数反演提供了理论依据.     

基于粒子群优化的多目标体中梯电阻率异常反演

在采用中梯装置的电阻率剖面法应用中,特别是在环境、水文和工程等领域,经常遇到需要对多个异常目标体进行快速定位以便及时进行相应处理的情况.利用倾斜椭球体来近似模拟这些电阻率异常目标体,使得正演计算可以采用解析表达式来实现,提高正演计算的时效性.相应的地球物理模型也可简化为由椭球体个数、中心点位置、倾角、轴径以及电阻率等参数构成的粒子,多个粒子组成的粒子群在粒子群优化算法的控制下在给定的搜索空间中并行地搜索最优模型.通过对粒子群优化算法参数的合理设计,利用其良好的全局与局部均衡的搜索能力实现对多个异常目标体的同时反演.数值实验结果表明该反演方法能有效实现对多个目标体的同时反演,计算速度快、反演拟合精度较高、同时具有一定的抗噪音能力.快速的多目标体反演,可以实时准确的定量解译中梯剖面法圈定的异常目标体,较好地满足工程等领域的高时效性要求.     

Application of an artificial neural network to typhoon rainfall forecasting

A neural network with two hidden layers is developed to forecast typhoon rainfall. First, the model configuration is evaluated using eight typhoon characteristics. The forecasts for two typhoons based on only the typhoon characteristics are capable of showing the trend of rainfall when a typhoon is nearby. Furthermore, the influence of spatial rainfall information on rainfall forecasting is considered for improving the model design. A semivariogram is also applied to determine the required number of nearby rain gauges whose rainfall information will be used as input to the model. With the typhoon characteristics and the spatial rainfall information as input to the model, the forecasting model can produce reasonable forecasts. It is also found that too much spatial rainfall information cannot improve the generalization ability of the model, because the inclusion of irrelevant information adds noise to the network and undermines the performance of the network. Copyright © 2005 John Wiley & Sons, Ltd.     

基于粒子群优化的双力偶模型振幅谱反演方法及应用

发展了一种基于全波形振幅谱的频率域双力偶震源机制反演方法. 通过理论振幅谱与观测振幅谱的拟合搜寻断层面参数, 基于粒子群优化算法可以在较短的时间内得到稳定可靠的解． 数值试验表明, 在定位误差较大, 以及台站布局较差的情况下, 振幅谱反演仍可较为准确地得到震源机制, 并且由此计算得到的最优震源深度仍比较接近真实的震源位置． 使用该方法用2010年5月17日渤海ML4.0地震的震源机制进行了检验, 结果与加权P波初动解非常一致． 应用该方法对山东半岛及近海地区2003——2010年14次MLge;4.0地震震源机制进行了估计．      

Daily reservoir inflow forecasting using artificial neural networks with stopped training approach

In this paper, an early stopped training approach (STA) is introduced to train multi-layer feed-forward neural networks (FNN) for real-time reservoir inflow forecasting. The proposed method takes advantage of both Levenberg–Marquardt Backpropagation (LMBP) and cross-validation technique to avoid underfitting or overfitting on FNN training and enhances generalization performance. The methodology is assessed using multivariate hydrological time series from Chute-du-Diable hydrosystem in northern Quebec (Canada). The performance of the model is compared to benchmarks from a statistical model and an operational conceptual model. Since the ultimate goal concerns the real-time forecast accuracy, overall the results show that the proposed method is effective for improving prediction accuracy. Moreover it offers an alternative when dynamic adaptive forecasting is desired.     

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.     

基于粒子群优化的理论变异函数拟合方法研究

变异函数是地统计学中区域化变量空间结构分析和空间局部插值的主要分析工具.理论变异函数模型的获取是地质统计学中的基础性工作,它是了解区域化变量的变异特征、进一步对地质统计学计算的必要环节.针对现有的理论变异函数的拟合方法,如人工拟合法、线性规划拟合法、加权多项式拟合法、目标规划拟合法等的不足之处,充分利用粒子群优化算法在求解非线性优化问题时具有的全局寻优的特点,提出基于粒子群优化的理论变异函数拟合方法.在实例应用中,分别利用粒子群优化算法和加权多项式拟合方法进行理论变异函数拟合,交叉验证结果表明粒子群优化算法预测精度较高,具有较强的稳健性.     

Performance of an artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a tropical site

Performance of a feed‐forward back‐propagation artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a single meteorological station is presented. Both short‐term and long‐term forecasting was attempted, with ground level data collected by the meteorological station in Colombo, Sri Lanka (79° 52′E, 6° 54′N) during two time periods, 1994–2003 and 1869–2003. Two neural network models were developed; a one‐day‐ahead model for predicting the rainfall occurrence of the next day, which was able to make predictions with a 74·3% accuracy, and one‐year‐ahead model for yearly rainfall depth predictions with an 80·0% accuracy within a ± 5% error bound. Each of these models was extended to make predictions several time steps into the future, where accuracies were found to decrease rapidly with the number of time steps. The success rates and rainfall variability within the north‐east and south‐west monsoon seasons are also discussed. Copyright © 2007 John Wiley & Sons, Ltd.     

多层及高层框架结构地震损伤诊断的神经网络方法

本文提出了强震后多层及高层框架结构地震损伤诊断的神经网络方法。文中在提出有结点损伤的梁柱有限元刚度矩阵的基础上，建立了有结点损伤框架结构的有限元模型。通过完好结构和有损伤结构的有限元分析，获取二者应变模态差值作为损伤标识量，并输入径向基（RBF）神经网络进行训练，得到了框架结构结点损伤诊断的神经网络系统。数值仿真分析结果表明，此神经网络可以对多层及高层框架结构结点各种程度的损伤做出成功诊断。     

Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models

Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes.     

建筑结构利用TLCD减振的神经网络智能控制

本文提出了建筑结构利用调谐液体柱型阻尼器（ＴＬＣＤ）减振的神经网络智能控制方法。首先阐述了确定ＴＬＣＤ半主动控制策略;然后利用ＢＰ人工神经网络方法计算并控制ＴＬＣＤ隔板孔洞的面积,以调节和控制阻尼比＆Ｔ,实现对建筑结构的智能控制。地震作用下的数值分析表明,本文所述的方法是十分有效的。     

Stormwater quantity and quality response to climate change using artificial neural networks

This research investigates the potential impacts of climate change on stormwater quantity and quality generated by urban residential areas on an event basis in the rainy season. An urban residential stormwater drainage area in southeast Calgary, Alberta, Canada is the focus of future climate projections from general circulation models (GCMs). A regression‐based statistical downscaling tool was employed to conduct spatial downscaling of daily precipitation and daily mean temperature using projection outputs from the coupled GCM. Projected changes in precipitation and temperature were applied to current climate scenarios to generate future climate scenarios. Artificial neural networks (ANNs) developed for modelling stormwater runoff quantity and quality used projected climate scenarios as network inputs. The hydrological response to climate change was investigated through stormwater runoff volume and peak flow, while the water quality responses were investigated through the event mean value (EMV) of five parameters: turbidity, conductivity, water temperature, dissolved oxygen (DO) and pH. First flush (FF) effects were also noted. Under future climate scenarios, the EMVs of turbidity increased in all storms except for three events of short duration. The EMVs of conductivity were found to decline in small and frequent storms (return period < 5 years); but conductivity EMVs were observed to increase in intensive events (return period ≥ 5 years). In general, an increasing EMV was observed for water temperature, whereas a decreasing trend was found for DO EMV. No clear trend was found in the EMV of pH. In addition, projected future climate scenarios do not produce a stronger FF effect on dissolved solids and suspended solids compared to that produced by the current climate scenario. Copyright © 2010 John Wiley & Sons, Ltd.     

Prospective scenarios of the saltwater intrusion in an estuary under climate change context using Bayesian neural networks

In recent decades, saltwater intrusion over some low-lying coastal regions was deteriorated by rising sea-level and decreasing streamflow in the context of climate change. Though physically-based hydrodynamic models are the most detailed means to simulate salinity processes, they are commonly restricted by data insufficiency issues both in spatial resolution and temporal lasting. This motivates us to build a statistical model enable simulation and scenario analysis for coastal salinity change with limited observations. A Bayesian neural network (BNN) model is built hereby to simulate salinity. It offers more precise estimation compared with the conventional artificial neural network. Meanwhile, the model gives the uncertainty behaviors of the final salinity simulation which is not available for other methods. Future scenarios of salinity change are constructed and analyzed in different time periods on the basis of the validated BNN model. Results indicate that the water quality over lower Pearl River is degrading along with more significant uncertainties. Further analysis suggests that streamflow alteration has a more direct impact on salinity variations than the sea-level change does. The method allows a profound analysis of the potential influence on water quality degradation in coastal and low-lying regions in support of water management and adaptation toward global climate change.     

Simulation of spring flows from a karst aquifer with an artificial neural network

In China, 9·5% of the landmass is karst terrain and of that 47,000 km² is located in semiarid regions. In these regions the karst aquifers feed many large karst springs within basins of thousands of square kilometres. Spring discharges reflect the fluctuation of ground water level and variability of ground water storage in the basins. However, karst aquifers are highly heterogeneous and monitoring data are sparse in these regions. Therefore, for sustainable utilization and conservation of karst ground water it is necessary to simulate the spring flows to acquire better understanding of karst hydrological processes. The purpose of this study is to develop a parsimonious model that accurately simulates spring discharges using an artificial neural network (ANN) model. The karst spring aquifer was treated as a non‐linear input/output system to simulate the response of karst spring flow to precipitation and applied the model to the Niangziguan Springs, located in the east of Shanxi Province, China and a representative of karst springs in a semiarid area. Moreover, the ANN model was compared with a previous time‐lag linear model and it was found that the ANN model performed better. Copyright © 2007 John Wiley & Sons, Ltd.     

基于粒子群优化算法与厄米多项式构建地震综合预测投影寻踪回归模型

在地震综合预测投影寻踪研究工作中,投影寻踪回归算法是其中应用最多的一种方法．但一般投影寻踪回归算法构造技术较为复杂,采用多次局部光滑回归,计算量较大,外推较为繁杂,容易陷于局部解．在综合考虑传统投影寻踪回归算法特点的基础上,针对投影寻踪回归计算中存在的一些不利因素,给出了一定的解决思路:采用粒子群优化算法代替高斯 牛顿算法优化投影方向;采用厄米多项式代替分段线性光滑回归来拟合岭函数,以简化优化过程;参数优化无需分组,获得全局优化的岭函数．利用数值仿真技术进行基于粒子群优化算法与厄米多项式构建的投影寻踪回归模型建模能力与计算精度的检验,再将其应用于多维地震时间序列和一般多维无序地震样本回归综合建模预测中．通过计算和分析表明,基于粒子群优化算法与厄米多项式构建的投影寻踪回归模型具有简单、快速、有效的特点,在实际地震综合预测建模中取得了满意的效果,可作为地震预测的一种综合分析方法．