BP神经网络论文：石羊河流域农业需水量预测及水资源优化配置研究.doc

BP神经网络论文：石羊河流域农业需水量预测及水资源优化配置研究【中文摘要】位于西北干旱地区的石羊河流域是全国水资源短缺的主要流域之一。流域需水量以农业灌溉为主,近年来随着气候的变化,人口的增加,各个行业需水量也在不断的变化。水资源短缺势必造成各个用水部门争水的不良的现象,为了实现流域的可持续发展,有必要对流域的农业需水量和水资源的优化配置进行研究。本文通过收集到的资料,对流域内的农业需水情况进行研究得出如下成果:(1)运用收集到的气象和作物资料,首先通过BP神经网络预测模型,建立了基于影响农业需水量的11个影响因子在民勤、天祝和全流域的农业需水量预测模型,经检验模型精度较高。(2)由于BP神经网络模型建模需要的资料量大,势必造成运算的繁琐。为了能在基础资料较少的情况下,对农业需水量很好的预测,本文通过对影响流域农业需水量的11个影响因素进行相关性分析,确定了这些因素和农业需水量的相关性,确立了影响流域需水量最重要的2个影响因子,即耕地面积和降水量;以及对流域需水量有明显影响作用的6个影响因子,即耕地面积、降水、粮食作物面积、积温、日照和年最高温。(3)通过多元回归分析,建立了基于六个主要影响因素的流域需水量六元线性回归模型。通过对影响因子的进一步优化进而建立了基于两个最重要影响因子的二元线性回归模型和BP神经网络模型,并用19992003年这5年的数据进行精度检验,发现BP神经网络的预测效果要好于二元线性回归模型。(4)运用灰色预测、指数平滑预测和二者的组合预测,通过只对历年农业需水量的分析,建立了石羊河流域农业需水量的预测模型,对三种模型进行精度检验,发现灰色预测的平均相对误差绝对值为4.84%,二次指数平滑预测的平均相对误差绝对值为6.14%,组合预测模型的的平均相对误差绝对值最小,为4.04%。用确定的组合预测模型对全流域未来十年的农业需水量进行预测,预测流域2004年流域的农业需水量为17.677108m3,到2013年需水量将达到19.178108m3。(5)通过考虑了流域的经济效益、社会效益和生态效益,以流域综合效益最大作为目标,利用农作物种植结构的多目标模糊优化模型原理,建立作物种植结构的多目标模糊优化模型,通过确立的目标函数,在面积和水量2个约束条件对目标函数进行求解,从而确定了流域综合效益最大下的主要作物种植面积。【英文摘要】Shiyang River Basin, which is located in the northwest arid region, is one of the main basins where there exists water shortage. Water demand bases mainly on agricultural irrigation in the basin, which of each industry constantly varies as the climate changes and the population increases in recent years. Water shortage certainly leads to the bad phenomenon that the departments of water consumption fight for water. Therefore, it is necessary to conduct the study of agricultural water requirements and optimal allocation of water resources in order to realize the aim of sustainable development. In the paper, agricultural water use in the basin was studied by collected data, concluding the following results:(1)Meteorological data and crop data collected were used to establish the model of agricultural water demand prediction on the basis of 11 factors influencing agricultural water demand in Minqin, Tianzhu and the whole basin. The accuracy of the model was superior by testing.(2)There needed a large number of data to establish BP neural network model, which brought about tedious operation. The correlation analysis of 11 factors was conducted that influenced agricultural water requirements so as to forecast agricultural water demand in the cases of fewer data. Then these factors and the correlation of agricultural water demand were determined as well as the most important factors affecting the water requirements of the basin, that is, cultivated area and precipitation. 6 factors that obviously influencing water demand were also ascertained, namely cultivated area, precipitation, food crop area, accumulated temperature, sunshine and the average annual maximum temperature.(3)The hexatomic linear regression models of water requirements were established on the basis of 6 main factors multiple regression analysis by multiple regression analysis. The influencing factors were further optimized, thereby setting up binary linear regression model and BP neural network based on the two best important factors. And five-year data from 1999 to 2003 were used to check up the accuracy, which proved that the prediction effect of BP neural network model was better than that of binary linear regression model.(4)The grey model, exponential smoothing model and their combined model were used to predict water demand. The forecasting model was built in the Shiyang River Basin by analyzing agricultural water requirements over the years. The precision of the three models was tested. Then it was dictated that the absolute of the average relative error of grey model was 4.84percent, binary exponential smoothing model 6.14percent and the combined model, the minimum of the three, 4.04percent. The combined model ascertained was utilized to predict the agricultural water demand in the basin in the coming 10 years. The prediction value in 2004 was 17.677108m3, and it reached 19.178108m3 in 2013.(5)Economic benefit, social benefit and ecological benefit were taken into account and the maximum comprehensive benefit was its final objective. The theory of the multi-objective fuzzy optimal model of the crop planting structure was used to establish the multi-objective fuzzy optimal model of the crop planting structure. The object function was solved by the object function established in the two conditionsarea and water yield. Thus, the main crop planting area was determined in the condition of the maximum comprehensive benefit.【关键词】BP神经网络 组合预测模型 水资源优化配置 农业需水量【英文关键词】BP neural network combined prediction model optimal allocation of water resources agricultural water demand【目录】石羊河流域农业需水量预测及水资源优化配置研究摘要5-6ABSTRACT6-7第一章 绪论10-171.1 研究背景及意义10-111.1.1 研究的背景10-111.1.2 研究意义111.2 需水量预测的研究进展11-131.2.1 国外研究现状11-121.2.2 国内研究现状12-131.3 农业水资源优化配置的研究进展13-151.4 研究内容、方法与技术路线15-171.4.1 研究内容和方法151.4.2 相关资料搜集151.4.3 技术路线图15-17第二章 流域农业需水量影响因素的变化分析17-242.1 研究区概况17-182.1.1 自然地理状况172.1.2 水资源状况17-182.1.3 农作物种植情况182.2 流域典型区域的选取18-192.3 流域农业需水量影响因素的变化19-242.3.1 作物种植面积的变化19-202.3.2 气象资料的变化20-24第三章 基于BP 神经网络的农业需水量预测24-303.1 模型介绍24-263.2 建立预测模型263.3 模型精度的检验26-293.4 本章小结29-30第四章 基于回归分析的农业需水量预测30-364.1 流域农业需水量影响因素的相关性分析30-324.2 多元线性回归分析324.2.1 回归分析的原理324.2.2 多元线性回归数学模型324.3 六元线性回归32-334.4 二元线性回归334.5 两种模型的精度比较33-344.6 本章小结34-36第五章 基于组合模型的农业需水量预测36-425.1 灰色预测模型36-375.2 指数预测模型37-385.2.1 一次指数平滑模型37-385.2.2 二次指数平滑模型385.2.3 三次指数平滑模型385.3 组合预测模型38-395.4 农业需水量预测39-415.4.1 灰色预测395.4.2 指数平滑预测39