坡面产流模式的神经网络模拟

坡面产流是土壤本身特性与外界影响因素相互作用的结果，它们之间具有明显的非线性输入输出关系。在分析坡面产流和神经网络模型具有某些相似的基础上，利用径流站观测资料，建立了小流域坡面产流量的三层前向网络模型（BP算法），并显示了具有较好的模拟预测效果。     

坡面降雨入渗产流规律的数值模拟研究

本文采用运动波理论和两次改进后的Green-Ampt入渗模型建立了坡面降雨入渗产流的动力学模型，并得到了实验资料的良好验证。运用该模型研究了简单坡面上降雨入渗产流的动力学规律，分析了雨强，土壤初始含水量，渗透系数，坡面阻力，以及坡长，坡度等因素对坡面产流过程的影响规律，得出了一些有益的结论。     

植被坡面产流特征及其侵蚀动力研究进展

植被覆盖变化状况直接影响流域水文过程,通过对黄土高原植被坡面产流特性已有研究成果进行总结,以期为评价植被对水沙调控的作用提供参考。从黄土高原生态建设和水土保持效益评价的需求出发,回顾了植被作用下坡面产流特征和侵蚀动力学特性研究的主要进展,包括植被增强土壤入渗能力和减缓径流的作用、植被对坡面产流的影响、植被作用下坡面流侵蚀动力学特性;明确了需进一步深化研究的主要方向,包括植被作用下坡面降雨-入渗非线性过程、坡面产流机制发生胁变的被覆临界、不同产流机制的侵蚀动力参数特性等。     

混合产流模型及其应用

华北地区呈半湿润、半干旱状态，产流计算模型，既不宜单独采用湿润地区的蓄满产流模型，也不直单独采用干旱地区的超渗产流模型，因此，根据蓄满产流和超渗产流的产流原理，综合利用二者的计算方法，提出了混合产流模型。     

平原城市雨洪过程模拟

本文提出一个适合平原城市水文过程模拟的数学模型，由产流、坡面汇流、管网汇流、河网汇流4个子模型组成.对北京市太平湖排水小区雨洪过程模拟验证表明，结果是可靠和合理的.     

TOPMODEL模型在半湿润地区径流模拟分析中的应用及改进

为了使TOPMODEL模型结构更合理,并能够用于半湿润地区或半干旱地区的径流过程模拟,文章对TOPMODEL模型的蒸发产流模块以及汇流模块进行改进,在蒸发产流模块中添加植被冠层截留蒸散发模型和Holtan超渗产流模型,汇流模块中坡面汇流采用瞬时单位线模型,河道汇流采用马斯京根河道洪水演进模型。通过对半湿润地区流域内降雨径流过程的模拟验证,表明通过对TOPMODEL模型的改进,模型对半湿润地区的降雨径流过程模拟精度有很大的提高,拓展了TOPMODEL模型适用范围。     

TOPMODEL模型改进及应用研究

为了使TOPMODEL模型结构更合理,并能够用于半湿润地区或半干旱地区的径流过程模拟,文章对TOPMODEL模型的蒸发产流模块以及汇流模块进行改进,在蒸发产流模块中添加植被冠层截留蒸散发模型和Holtan超渗产流模型,汇流模块中坡面汇流采用瞬时单位线模型,河道汇流采用马斯京根河道洪水演进模型。通过对半湿润地区流域内降雨径流过程的模拟验证,表明通过对TOPMODEL模型的改进,模型对半湿润地区的降雨径流过程模拟精度有很大的提高,拓展了TOPMODEL模型适用范围。     

黄土地区土壤含水量消退系数的变化规律与产流计算

土壤前期影响雨量的消退系数K是产流模型中的参数之一，它直接影响产流成果。以南京水文出资源所产流模型为例，对团山沟流域的K值与产流进行了计算，并得到如下认识：1）单点K值与土层深度成线性递增关系，而与初始土壤含水量呈递减关系。只有当土层取得足够厚时，计算的K值才能够较确切地反映蒸散发的影响，这个土层深度要依靠降雨入渗锋面的分析来确定；2)数域面平均K值应与数城产流模型结合考虑，作为模型参数优选确定。     

雨强和地表糙度对坡面微地形及侵蚀的影响

地表糙度是影响坡面侵蚀产沙的重要因素之一,以往研究多关注糙度对坡面产流产沙特征的影响,而较少关注不同糙度条件下坡面微地形变化和侵蚀产沙的关系。通过人工模拟降雨试验,结合Photoscan技术研究了不同雨强和地表糙度对坡面微地形及产流产沙的影响。结果表明:在试验条件下,降雨后光滑坡面和粗糙坡面4个微地形因子(地表糙度、地形起伏度、地表切割度、洼地蓄积量)数值均减小,且有随雨强增大,其减幅增大的趋势;相同雨强和降雨历时条件下,粗糙坡面微地形因子变化幅度大于光滑坡面,微地形因子变化量与侵蚀产沙量呈明显正相关;与光滑地表相比,粗糙地表只在降雨初期能有效减少产流,随着降雨时间延长,2种坡面的产流率趋于一致;在试验选取的4个雨强条件下,粗糙坡面和光滑坡面产流率均呈现先增大后趋于稳定的趋势。粗糙坡面产沙率和产流率变化规律一致,但光滑坡面产沙率表现出在产流初期迅速增大,而后呈降低并趋于稳定的趋势。研究结果可为揭示坡面土壤侵蚀机理和建立坡面侵蚀产沙模型提供参考。     

地表条件对坡面产流的影响

本文将复杂地表条件对坡面流运动的影响概化为阻力的变化，运用运动波理论和修正的Green-Ampt入渗模型，建立了能够反映地表条件影响的坡面降雨入渗产流模型，数值结果与实验符合较好，运用该模型分析了植被、地形、坡长、坡度等地表条件对坡面产流过程的影响。     

Simulation of rainfall-underground outflow responses of a karstic watershed in Southwest China with an artificial neural network

Karstic aquifers in Southwest China are largely located in mountainous areas and groundwater level observation data are usually absent. Therefore, numerical groundwater models are inappropriate for simulation of groundwater flow and rainfall-underground outflow responses. In this study, an artificial neural network （ANN） model was developed to simulate underground stream discharge. The ANN model was applied to the Houzhai subterranean drainage in Guizhou Province of Southwest China, which is representative of karstic geomorphology in the humid areas of China. Correlation analysis between daily rainfall and the outflow series was used to determine the model inputs and time lags. The ANN model was trained using an error backpropagation algorithm and validated at three hydrological stations with different karstic features. Study results show that the ANN model performs well in the modeling of highly non-linear karstic aquifers.     

短历时降雨强度对黄土坡地径流形成影响的实验研究

雨强对坡地降雨径流形成过程有明显影响。本文利用室内控制精度较高的实验设施，重复进行了黄土坡地降雨径流形成过程的实验，积累了较详尽的观测资料。通过实验观测数据与数值模拟计算结果的比较，系统分析了短历时降雨强度对坡地入渗、坡面产流和坡面漫流过程的影响。阐明了短历时雨强对入渗的影响可分为两阶段：地表积水前雨强对入渗量及湿锋下移速率有明显影响；积水后影响甚小可忽略不计。黄土坡地表层经常处于干燥状态，入渗能     

Artificial neural network modeling for identification of unknown pollution sources in groundwater with partially missing concentration observation data

Groundwater pollution sources are characterized by spatially and temporally varying source locations, injection rates, and duration of activity. Concentration measurement data at specified observation locations are generally utilized to identify these sources characteristics. Identification of unknown groundwater pollution sources in terms of these source characteristics becomes more difficult in the absence of complete breakthrough curves of concentration history at all the time steps. If concentration observations are missing over a length of time after an unknown source has become active, it is even more difficult to correctly identify the unknown sources. An artificial neural network (ANN) based methodology is developed to identify these source characteristics for such a missing data scenario, when concentration measurement data over an initial length of time is not available. The source characteristics and the corresponding concentration measurements at time steps for which it is not missing, constitute a pattern for training the ANN. A groundwater flow and transport numerical simulation model is utilized to generate the necessary patterns for training the ANN. Performance evaluation results show that the back-propagation based ANN model is essentially capable of extracting hidden relationship between patterns of available concentration measurement values, and the corresponding sources characteristics, resulting in identification of unknown groundwater pollution sources. The performance of the methodology is also evaluated for different levels of noise (or measurement errors) in concentration measurement data at available time steps.     

基于一维运动波理论的坡面汇流和沟道汇流数学模型研究

一维运动波理论常用于坡面汇流和沟道汇流的模拟,根据坡面汇流和沟道汇流的特点,采用不同形式的运动波控制方程组来分别描述坡面汇流和沟道汇流过程。采用Mac Cormack格式开发了坡面汇流模型,采用线性离散和非线性离散的显式有限差分格式开发了沟道汇流模型,通过3个典型的算例对各模型进行了验证。验证结果表明,开发的沟道汇流模型是成功的;空间步长的尺度对数值解精度有较大影响,步长越小,数值解精度越高;在沟道汇流模型中,线性求解和迭代求解两种方法的计算结果基本一致。     

Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks

The applicability of artificial neural networks (ANN) for modelling of daily river flows in a humid tropical river basin with seasonal rainfall pattern is investigated and the model performance assessed using the commonly adopted efficiency indices. Although the developed model showed satisfactory results for rainy period, the predicted hydrograph for the low flow period deviate from the observed data considerably. The rainfall and discharge data available for modelling is explored using Self Organizing Maps (SOM) and the subset of data having definite relationship between the selected hydrologic variables identified. The alternate approach for modelling of river flows utilising the knowledge from SOM analysis has improved the model results. The results show that ANN models can be adopted for forecasting of river flows in the humid tropical river basins for the monsoon period. Input data exploration using SOM is found helpful for developing logically sound ANN models.     

A self-tuning ANN model for simulation and forecasting of surface flows

Artificial neural networks (ANN) are applicable for and forecasting without the need to calculate complex nonlinear functions. This paper evaluates the effectiveness of temperature, evapotranspiration, precipitation and inflow factors, and the lag time of those factors, as variables for simulating and forecasting of runoff. The genetic algorithm (GA) is coupled with ANN to determine the optimal set of variables for streamflow forecasting. The minimization of the total mean square error (MSE) is considered as the objective function of the ANN-GA method in this paper. Our results show the effectiveness of the ANN-GA for simulating and forecasting runoff with consistent accuracy compared with using pure ANN for runoff simulation and forecasting.     

Comparative Evaluation of Genetic Programming and Neural Network as Potential Surrogate Models for Coastal Aquifer Management

Determining the optimal rates of groundwater extraction for the sustainable use of coastal aquifers is a complex water resources management problem. It necessitates the application of a 3D simulation model for coupled flow and transport simulation together with an optimization algorithm in a linked simulation-optimization framework. The use of numerical models for aquifer simulation within optimization models is constrained by the huge computational burden involved. Approximation surrogates are widely used to replace the numerical simulation model, the widely used surrogate model being Artificial Neural Networks (ANN). This study evaluates genetic programming (GP) as a potential surrogate modeling tool and compares the advantages and disadvantages with the neural network based surrogate modeling approach. Two linked simulation optimization models based on ANN and GP surrogate models are developed to determine the optimal groundwater extraction rates for an illustrative coastal aquifer. The surrogate models are linked to a genetic algorithm for optimization. The optimal solutions obtained using the two approaches are compared and the advantages of GP over the ANN surrogates evaluated.     

坡面流阻力研究进展

坡面流阻力的研究对了解坡面流水流特性、汇流过程、土壤侵蚀和坡面产沙机理都非常重要。本文分5个方面总结了坡面流阻力研究的最新进展:(1)坡面流阻力系数与雷诺数的关系;(2)光滑床面上的坡面水流阻力;(3)粗糙床面上的坡面水流阻力;(4)降雨对坡面流阻力影响的研究;(5)坡面流阻力的计算模型。综合前人研究发现,阻力系数与雷诺数在不同的条件下呈正相关或负相关关系;用叠加法计算不同条件下的坡面流阻力有其合理的一面,也有不足之处;粗糙动床条件下的坡面水流阻力计算模型更接近实际情况。最后,本文指出了当前坡面流阻力研究存在的主要问题和未来需要突破的主攻方向。     

Deterministic Insight into ANN Model Performance for Storm Runoff Simulation

The artificial neural network (ANN) theory has been widely applied to practical applications in hydrology. Since watershed rainfall–runoff processes are nonlinear and exhibit spatial and temporal variability, the ANN model, which considers watershed nonlinear characteristics, can usually but not always obtain satisfactory simulation results. The training of an ANN network is based completely on the reliability of the available hydrologic records. The objective of this study was to provide deterministic insight into the limitations of storm runoff simulation when using ANN. Hydrologic records of 42 storm events from two watersheds in Taiwan were adopted for analysis. A deterministic runoff model was used to classify the hydrologic records into “usual” and “unusual” storm events. The analytical results show that the ANN model could provide good simulation results for “usual” storm events; however, its performance was poor when it was applied to “unusual” storm events because no consistent hydrologic characteristics could be extracted from the storm event records using ANN. The success of the ANN model in usual storm discharge simulations may be mainly due to the input vectors including the previous observed discharge. Moreover, the number of past periods of rainfall that were set as the input vectors of the ANN model was found to be highly correlated with the watershed time of concentration. It can be used to efficiently determine the ANN network structure instead of using iterative network training.