Jensen模型水分敏感指数的新定义及其解法

在总结已有的作物水分生产函数Jensen模型研究工作的基础上,针对以往研究中水分敏感指数与生育阶段划分密切相关的问题,对Jensen模型进行了改造,提出了水分敏感指数的新定义。并在Jensen模型水分敏感指数的累加性分析的基础上,提出了确定Jensen模型水分敏感指数及累积曲线的更为简洁的新方法,新方法得到了田间试验的验证并与传统方法有很好的一致性。     

冬小麦水分生产函数Jensen模型敏感指数的研究

根据冬小麦返青后田间水量平衡估算出田间作物实际腾发量和最大腾发量,由此,对水分生产函数Jensen分阶段连乘模型中的敏感指数进行了拟合分析。研究表明,Jensen模型中的敏感指数与作物生长阶段以及该阶段长短占总生长期的比例有关,但冬小麦返青后各阶段的敏感指数的累计值可以表示为时间t的一元函数,且与阶段划分无关,由该函数可得到任一阶段的敏感指数值。分析结果还表明,Jensen模型可以较好地反映作物田间耗水量与产量的关系。     

作物水分氮素生产函数模型的研究

在水分生产函数动态产量模型的基础上,考虑土壤氮素对作物生长的影响,建立了作物水分 氮素生产函数动态产量模型,根据作物生长过程中水分和氮素状态,可以对干物质生长过程进行跟踪和预测预报,利用北京永乐店试验资料进行模型参数的拟合,并应用该模型对不同生育阶段干物质产量进行预测,效果良好。     

作物水分与氮素生产函数的实验研究

在田间水肥耦合试验的基础上,对水分生产函数的概念加以拓宽,提出了水分、氮素生产函数的概念,并建立了最终产量模型和动态产量模型,进行了参数求解,模型模拟结果和田间试验结果符合较好.     

作物水分生产函数与农田非充分灌溉研究述评

介绍作物水分生产函数国内外研究现状,把目前通用的各类模型归纳为最终产量模型和动态产量模型两大类,分析评述两类模型的特点及适用条件.此外,对作物水分生产函数的试验方法和试验处理设计作了介绍.     

考虑水分胁迫后效应的作物水模型

水分胁迫具有后效应,前期水分胁迫可以影响作物后期叶面积和需水量的增减。以Jensen作物 水模型为基础,引入了水分胁迫后效应影响系数,对原模型进行了修正。修正后的模型可以将阶段水分胁迫与前期胁迫后效应对产量的影响加以区分,避免了原模型中可能产生的虚缺水现象,并可对作物(以玉米为例)前期水分胁迫处理后,后期需水量增加以及苗期胁迫处理可维持较高产量的原因进行合理解释。通过田间试验结果分析,改进后模型的模拟结果符合实际,并具有较好的精度。对模型存在的问题和不足也进行了探讨。     

水稻水分生产函数时空变异规律研究

基于对水稻Jensen模型中敏感指数在全生育期变化规律的认识,以生长曲线函数建立了水稻敏感指数累积函数,分析了其特征,从而解决了不同长时段敏感指数转换计算问题。较全面地揭示了水稻水分生产函数及其敏感指数累积函数中主要参数随气象条件及土壤因子变化的规律。通过参照作物需水量及其频率以及不同地区土壤有效含水量为媒介,建立了对水分敏感指标在不同水文年份 (时间)和不同地区 (空间)进行预报的数学模型,据此提出了水稻水分生产函数在时、空两方面插补、延长、移用与扩展的理论与方法。借助于参照作物需水量等值线图及土壤分布图,探讨了绘制水分生产函数及其主要参数等值线图的原理和方法。     

非充分灌溉制度设计优化模型

研究了缺水地区冬小麦灌溉问题.分析了作物模型,作物水分影响函数,并以农作物产量最大为目标,提出了非充分灌溉制度优化设计二维动态规划模型和相应的动态规划逐次逼近(DPSA)求解方法.针对山东省临沂市小埠东灌区的实际情况进行研究,求得了冬小麦三个典型年不同供水水平的最优灌溉制度、排水过程及相应产量.实例表明,模型及方法是合理的.     

灌区干旱风险评估模型研究

根据风险理论,建立了包括农业干旱发生概率、抗旱能力、受灾体种植面积比等多因子的灌区农业干旱风险评估模型。并将相对产量作为灌区农业干旱评估指标,能够反映土壤 作物 大气系统中水分运动对农业生产的影响,利用该指标结合干旱风险评估模型对灌区农业干旱进行风险评估,分析出灌区各种作物对干旱风险度影响最大的生育阶段和风险度最高的农作物,以便灌区制定合理的抗旱方案以减小灌区干旱损失。     

微咸水膜下滴灌对沙漠温室黄瓜产量的影响研究

膜下滴灌技术在干旱地区得到了广泛的推广。黄瓜是目前种植面积最大的温室蔬菜之一。此外,水分是影响黄瓜生长和品质的重要因素。西北干旱地区淡水资源总量不足,但咸水资源丰富,因此,在我国西北找到适合黄瓜咸水灌溉方案至关重要。在沙漠温室膜下滴灌条件下,采用咸水灌溉黄瓜。本研究揭示了黄瓜产量及黄瓜水盐生产模型。本文研究了黄瓜产量与作物需水量、土壤电导率等生产函数的估算问题。利用田间试验数据估算作物水盐生产函数,为在我国西北干旱地区,特别是宁夏回族自治区推广咸水膜下滴灌技术提供了科学依据。     

Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model

A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, E_V).

The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding.     

An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia

Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R ²), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor.     

Evaluation of water quality parameters for the Mamasin dam in Aksaray City in the central Anatolian part of Turkey by means of artificial neural networks

Sustaining the human ecological benefits of surface water requires carefully planned strategies for reducing the cumulative risks posed by diverse human activities. The municipality of Aksaray city plays a key role in developing solutions to surface water management and protection in the central Anatolian part of Turkey. The responsibility to provide drinking water and sewage works, regulate the use of private land and protect public health provides the mandate and authority to take action. The present approach discusses the main sources of contamination and the result of direct wastewater discharges into the Melendiz and Karasu rivers, which recharge the Mamasın dam sites by the use of artificial neural network (ANN) modeling techniques. The present study illustrates the ability to predict and/or approve the output values of previously measured water quality parameters of the recharge and discharge areas at the Mamasin dam site by means of ANN techniques. Using the ANN model is appreciated in such environmental research. Here, the ANN is used for estimating if the field parameters are agreeable to the results of this model or not. The present study simulates a situation in the past by means of ANN. But in case any field measurements of some relative parameters at the outlet point “discharge area” have been missed, it could be possible to predict the approximate output values from the detailed periodical water quality parameters. Because of the high variance and the inherent non-linear relationship of the water quality parameters in time series, it is difficult to produce a reliable model with conventional modeling approaches. In this paper, the ANN modeling technique is used to establish a model for evaluating the change in electrical conductivity (EC) and dissolved oxygen (DO) values in recharge (input) and discharge (output) areas of the dam water under pollution risks. A general ANN modeling scheme is also recommended for the water parameters. The modeling process includes four main stages: (1) source data analysis, (2) system priming, (3) system fine-tuning and (4) model evaluation. Results of the ANN modeling scheme indicate that the output values are agreeable to the water quality parameters, which were measured at the field in the static water mass of the Mamasın dam lake. Water contamination at the dam site is caused by the continuous increase of nutrient contents and decrease of the O₂ level in water causing an anaerobic condition. It may stimulate algae growth flow in such water bodies, consequently reducing water quality.     

Predicting moisture content of soil from thermal properties using artificial neural network

Monitoring of soil moisture contents is an important practice for irrigation water management. The benefit of periodic soil water content data includes improved irrigation scheduling in order to optimize water usage for improved crop productivity. However, the in situ equipment for measuring soil water contents have high maintenance and operation cost and are highly affected by neighboring soil conditions, and some have overwhelming calibration and data interpretation, whereas the common standard laboratory procedure requires much effort and can be time-consuming for large dataset. The objective of this study is to evaluate the applicability of artificial neural network (ANN) to predict moisture content of soil using available or measured thermal properties (thermal conductivity, thermal diffusivity, specific heat, and temperature) of soil. We used both multilayered perception (MLP) and radial basis function (RBF) types of ANN. The study area is a farmland situated within the premises of the University of Ibadan campus. Thermal properties were measured with KD2 Pro at 42 points along seven transects. Soil samples were also collected at these points to determine their moisture contents in the laboratory. ANN analysis carried out effectively predicted the soil moisture content with very low root-mean-square error (RMSE) and high correlation coefficient (R) of approximately 0.9 for the two methods evaluated. The overall results suggest that ANN can be incorporated to predict the moisture content of soil in this area where thermal properties are known.     

RBFNN模型在渗透系数反演中的应用

针对经典的BP网络存在的一些缺陷,采用了径向基函数神经网络（RBFNN）。在相同的收敛条件下,用RBFNN和经典算法的BP网络进行了比较,表明前者具有优越性。在工程实例中,基于人工神经网络的非线性特点,在三维渗流有限元的基础上,利用RBFNN反演了大坝的渗透系数。并利用反演结果进行渗流场分析,水头预报值也有很高的精度,说明反演结果是正确的,从而,验证了RBFNN应用于反演分析中的可靠性。