Conceptual fuzzy neural network model for water quality simulation

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally‐defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN‐based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. Copyright © 2006 John Wiley & Sons, Ltd.     

A hybrid neural network model for typhoon-rainfall forecasting

A hybrid neural network model is proposed in this paper to forecast the typhoon rainfall. Two different types of artificial neural networks, the self-organizing map (SOM) and the multilayer perceptron network (MLPN), are combined to develop the proposed model. In the proposed model, a data analysis technique is developed based on the SOM, which can perform cluster analysis and discrimination analysis in one step. The MLPN is used as the nonlinear regression technique to construct the relationship between the input and output data. First, the input data are analyzed using a SOM-based data analysis technique. Through the SOM-based data analysis technique, input data with different properties are first divided into distinct clusters, which can help the multivariate nonlinear regression of each cluster. Additionally, the topological relationships among data are discovered from which more insight into the typhoon-rainfall process can be revealed. Then, for each cluster, the individual relationship between the input and output data is constructed by a specific MLPN. For evaluating the forecasting performance of the proposed model, an application is conducted. The proposed model is applied to the Tanshui River Basin to forecast the typhoon rainfall. The results show that the proposed model can forecast more precisely than the model developed by the conventional neural network approach.     

A neural network model for liquefaction-induced horizontal ground displacement

The horizontal ground displacement generated by seismically induced liquefaction is known to produce significant damage to engineered structures. A backpropagation neural network model is developed to predict the horizontal ground displacements. A large database containing the case histories of lateral spreads observed in eight major earthquakes is used. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the amount of horizontal ground displacement. As more data become available, the model itself can be improved to make more accurate displacement prediction for a wider range of earthquake and site conditions.     

一种新型的地下含水层涌水量预测模型

针对目前电法勘探预测地下含水层单孔单位涌水量方法中存在要求大样本、易出现过学习和局部极小等缺陷,基于支持向量回归机(Support vector regression,SVR)具有小样本、推广能力强、全局最优算法等优点,又可避免现有预测模型中的过学习和推广能力差等问题.本文利用支持向量回归机模型,由电测深法观测到的电阻率和激发极化等参数建立了预测地下含水层单孔单位涌水量模型,在已知抽水试验的井孔上与以往预测模型对比表明,该预测模型不但提高了预测精度,而且还具有很好推广能力和应用前景.     

结合温度因子估算太湖叶绿素a含量的神经网络模型

神经网络方法估算复杂水体水质参数的优越性已经得到证实.基于太湖水体实测叶绿素a浓度,利用MODIS 250m影像和反演得到的水温数据建立了估算太湖水体叶绿素a含量的两个单隐层BP神经网络模型:NN1模型不含温度因子、NN2模型包含温度因子,采用Levenberg-Marquardt算法训练网络,利用初期终止方法提岛网络泛化能力,均取得了较高估算精度,其中包含温度因了的反演模型精度稍有提高,但不显著.     

地震诱发的侧向水平位移神经网络预测模型

在对地震液化诱发的侧向水平位移预测模型评述的基础上，分析了地震、地形、土质等实测数据与侧向水平水移之间的相互关系，并提出了侧向位平位移神经网络预测模型。模型较好地反映了参数之间复杂的非线性关系，网络预测结果与实测数据较为吻合，两者之间相关系数为0．9左右。模型数据分析结果表明侧向位移随着距自由临空面距离（L）的增加而呈双曲线关系下降，随液化层厚度的增加而增加。不同L条件一临空面高度与侧向位移之间有一灵敏变化区，即当H约等于4－7m之间时，侧向位移急剧变化。     

A hybrid artificial neural network-numerical model for ground water problems

Numerical models constitute the most advanced physical-based methods for modeling complex ground water systems. Spatial and/or temporal variability of aquifer parameters, boundary conditions, and initial conditions (for transient simulations) can be assigned across the numerical model domain. While this constitutes a powerful modeling advantage, it also presents the formidable challenge of overcoming parameter uncertainty, which, to date, has not been satisfactorily resolved, inevitably producing model prediction errors. In previous research, artificial neural networks (ANNs), developed with more accessible field data, have achieved excellent predictive accuracy over discrete stress periods at site-specific field locations in complex ground water systems. In an effort to combine the relative advantages of numerical models and ANNs, a new modeling paradigm is presented. The ANN models generate accurate predictions for a limited number of field locations. Appending them to a numerical model produces an overdetermined system of equations, which can be solved using a variety of mathematical techniques, potentially yielding more accurate numerical predictions. Mathematical theory and a simple two-dimensional example are presented to overview relevant mathematical and modeling issues. Two of the three methods for solving the overdetermined system achieved an overall improvement in numerical model accuracy for various levels of synthetic ANN errors using relatively few constrained head values (i.e., cells), which, while demonstrating promise, requires further research. This hybrid approach is not limited to ANN technology; it can be used with other approaches for improving numerical model predictions, such as regression or support vector machines (SVMs).     

An improved neural network approach to the determination of aquifer parameters

In this paper, an artificial neural network (ANN) approach to the determination of aquifer parameters is developed. The approach is based on the combination of an ANN and the Theis solution. The proposed ANN approach has advantages over the existing ANN approach. It avoids inappropriate setting of a trained range. It also determines the aquifer parameters more accurately and needs less required training time. Testing the existing and the proposed ANN approaches by 1000 sets of synthetic data also demonstrates these advantages. As to the comparison between the proposed ANN approach and the type-curve graphical method, an application to actual time-drawdown data shows that the proposed ANN approach determines the aquifer parameters more precisely. The proposed ANN approach is recommended as an alternative to the type-curve graphical method and the existing ANN approach.     

Theoretic analysis on phase delay phenomena of well water level tide in the double medium aquifer model

Introduction The phenomenon of water level tide was discovered at Duchort diggings, Czech in 1879. By 1939, Theis, an America hydrogeologist, confirmed that periodical wave of the well water level is caused by the solid tide. In 1964, Melchior, a Belgium geophysist, began to make research on this phenomenon. Then Cooper (1965), Bredehoeft (1967) and WANG, et al (1988) followed. In China the study on water level tide began with 1970s, and the study on well water level phase lagging began …     

Estimating aquifer properties from the water level response to Earth tides

Water level fluctuations induced by tidal strains can be analyzed to estimate the elastic properties, porosity, and transmissivity of the surrounding aquifer material. We review underutilized methods for estimating aquifer properties from the confined response to earth tides. The earth tide analyses are applied to an open well penetrating a confined carbonate aquifer. The resulting range of elastic and hydraulic aquifer properties are in general agreement with that determined by other investigators for the area of the well. The analyses indicate that passive monitoring data from wells completed in sufficiently stiff, low porosity formations can provide useful information on the properties of the surrounding formation.     

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.     

昌黎井含水层系统的水位水温动态关系与地震活动

研究深层含水系统水位，水温动态，目前多以变化形态的描述和年动态特征的分析为主，本文根据水位，水温的变化关系，研究了两者线性相关系数r值的稳定和回归系数b值随时间的变化，发现地震活动平静时间段的r值和b值相对稳定，而在地城活动频敏，强度较高时间段为r值和b 值变化较大，文章对1989年10月19日大同6．1级地震和1995年以来地震活动性及1998年1月10日张北6．2级地震前后r值和b值的变化进行了讨论，显示了比单一分析水位或水温动态能够获得更多的信息，从对深层地下水动态与水温关系的分析认为，水位动态是深层含水系统热动平衡状态的反应，研究水位动态必须同时研究地下水热动态。     

预测人员震害损失的神经网络模型

随着区域经济的发展与城市化进程的加快,人口的集中度不断增加,这也给抗震防灾带来了新的课题。对地震中人员的损失进行有效的预测可为抗震防灾工作提供有力的管理方向。选择地震发生的震级、震源深度、震中烈度、设防水准、地震加速度、人口密度、地震预报等影响地震灾害人员伤亡的主要因素作为预测指标,以37次严重地震灾害为样本,建立了我国特征的BP神经网络地震灾害人员伤亡预测模型。     

由承压井水位动态反演水井含水层的应力变化

分析了承压井含水层应力场与地壳应力应变之间以及含水层应力应变与井水位之间的定量关系,给出了利用承压不位变化反演含水层应力变化的三种新方法,通过含水层的参数,固体潮效应以及气压效应反演水井含水层的应力变化,讨论了承压井水位反映含水层应力应变的稳定性,并将该反演结果与其它方法的结果进行了比较。     

Teleconnection and climatic oscillation in aquifer water level in Kumamoto plain,Japan

Relative little is known about the interaction between climate change and groundwater. Analysis of aquifer response to climatic variability could improve the knowledge related to groundwater resource variations and therefore provides guidance on water resource management. In this work, seasonal and annual variations of groundwater levels in Kumamoto plain (Japan) and their possible interactions with climatic indices and El Niño Southern Oscillation (ENSO) were analyzed statistically. Results show the following: (1) The water level in the recharge area mainly fluctuates at 1‐ and 2‐year periods, whereas the significant periodicity for water level oscillation in the coastal aquifer is 0.5 year. (2) The aquifer water levels are possibly influenced by variability in precipitation, air temperature, barometric pressure, humidity variances and ENSO. Relative high correlations and large proportions of similarities in wavelet power patterns were found between these variables and water levels. (3) Aquifer response to climatic variances was evaluated using cross wavelet transform and wavelet coherence. In recharging aquifers, the ENSO‐induced annual variations in precipitation, air temperature, humidity and barometric pressure affect aquifer water levels. The precipitation, air temperature and humidity respond to ENSO with a 4‐, 6‐ and 8‐month time lag, respectively, whereas the ENSO imparts weak influence on the barometric pressure. Significant biennial variation of water levels during 1991–1995 is caused primarily by precipitation and humidity variations. In the coastal aquifer, the 0.5‐year variability in ENSO is transferred by precipitation, barometric pressure and humidity to aquifer water levels, and the precipitation/humidity influence is more significant comparing with the barometric pressure. Copyright © 2014 John Wiley & Sons, Ltd.     

松散承压含水层水位的同震响应实验与数值模拟

为深入理解井水位同震响应机理,本文开展了向完整井-松散含水层系统输入由不同频率和振幅（加速度）组成的正弦波荷载的振动台实验。以实验模型为物理模型,建立了振动作用下松散承压含水层中孔隙水压力响应的流固耦合模型和含水层水流与井流的相互作用模型,并运用多物理场耦合模拟软件COMSOL Multiphysics对实验过程进行了数值模拟。实验中观测到的四种典型水位变化形态与野外场地同震井水位变化形态相似。数值模拟结果显示,本研究建立的数学模型能较好地反映松散承压含水层中孔隙水压力和水位的响应情况。本文研究对解释地下水同震响应机制、岩体渗流稳定性和安全问题具有重要意义。     

Observations on the application of artificial neural network to predicting ground motion measures

Application of the artificial neural network (ANN) to predict pseudospectral acceleration or peak ground acceleration is explored in the study. The training of ANN model is carried out using feed-forward backpropagation method and about 600 records from 39 California earthquakes. The statistics of the residuals or modeling error for the trained ANN-based models are almost the same as those for the parametric ground motion prediction equations, derived through regression analysis; the residual or modeling error can be modeled as a normal variate. The similarity and differences between the predictions by these two approaches are shown. The trained ANN-based models, however, are not robust because the models with almost identical mean square errors do not always lead to the same predictions. This undesirable behaviour for predicting the ground motion measures has not been shown or discussed in the literature; the presented results, at least, serve to raise questions and caution on this problem. A practical approach to ameliorate this problem, perhaps, is to consider several trained ANN models, and to take the average of the predicted values from the trained ANN models as the predicted ground motion measure.     

Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network

In this research, the simulation of Urmia Lake water level fluctuation by means of two models was applied. For this, Support Vector Machines (SVM), and Neural Wavelet Network (NWN) models that conjugated both the wavelet function and ANN, developed for simulating the Urmia Lake water level fluctuation. The yearly data of rainfall, temperature and discharge to the Urmia Lake and water level fluctuation were used. Urmia Lake is the biggest and the hyper saline lake in Iran. The outcome of the SVM based models are compared with the NWN. The results of SVM model performs better than NWN and offered a practical solution to the problem of water level fluctuation predictions. Analysis results showed that the optimal situation occurred with use of precipitation, temperature and discharge for all station and water level fluctuations at the lag time of one year (RMSEs) of 0.23, 0.41 m obtained by SVM, NWN, respectively, and SSEs of 0.43, 1.33 and R ² of 0.97, 0 obtained by SVM, NWN, respectively. The results of SVM model show better accuracy in comparison with the NWN model.