基于BP神经网络的玛纳斯河流域雪盖率预测

南京大学学报(自然科学版) ›› 2015, Vol. 51 ›› Issue (5): 1014–1021.

基于BP神经网络的玛纳斯河流域雪盖率预测

郑文龙,2,3，都金康1,2,3*

出版日期:2015-09-09 发布日期:2015-09-09
作者简介:(1. 江苏省地理信息技术重点实验室,南京大学,南京,210023; 2. 卫星测绘技术与应用国家测绘地理信息局重点实验室,南京大学,南京,210023; 3. 南京大学地理信息科学系,南京,210023)
基金资助:
国家自然科学基金项目（41271353），国家高分辨率对地观测系统重大专项项目（95-Y40B02-9001-13/15-04）

Forecasting snow cover fraction in Manasi River Basin based on BP neural network

Zheng Wenlong1,2,3Du Jinkang1,2,3*

Online:2015-09-09 Published:2015-09-09
About author:(1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, 210023, China; 2. Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing, 210023, China; 3. Department of Geographic Information Science, Nanjing University, Nanjing, 210023, China)

摘要/Abstract

摘要： 为了实现对新疆玛纳斯河流域的预测，为该区融雪径流预报提供数据支撑，本文选取反向传播（BP）神经网络模型时间序列预测的方法对研究区的预测问题进行了研究。首先基于2001-2012年研究区MODIS积雪数据产品MOD10A2提取研究区雪盖并计算，分析研究区变化在时间上的自相关性，据此选取对预测值影响较大的相邻若干个历史时刻的以及往年同期的均值作为模型输入变量，建立神经网络模型，并选取相关系数与确定系数作为模型预测效果的评价指标。结果表明：以BP神经网络时间序列预测方法预测2012年积雪覆盖率，输入变量为与待预测雪盖率相邻的前期与往年同期均值，预测值与实际值的相关系数为0.80，确定系数为0.61，预测结果基本反映了2012年的年内变化趋势，可以为融雪径流模型SRM）径流预报提供一定的数据支撑。

Abstract: Snow is an important factor of earth’s surface, it has great influence on the cold and arid area, especially western China. The purpose of the study is to predict the snow cover fraction in Manasi River Basin of Xinjiang Province, where snow melt water is one of the most important freshwater resources. In order to achieve good prediction accuracy, this paper presented a prediction method based on back propagation (BP) neural network. The snow cover data used in this study was extracted from MODIS snow products MOD10A2 from 2001 to 2012. To predict the snow cover fraction, several adjacent snow cover fractions of historic moment was selected by autocorrelation analysis. Besides the adjacent snow cover fractions, the input variables of the BP network also included the average snow cover fraction of precious years. Meanwhile, the coefficient of correlation and the coefficient of determination between predicted value and the actual value were chosen to evaluate the prediction effect of the model. The results showed that the BP network could reflect the general trend of 2012 snow cover fraction, and the coefficient of correlation could reach 0.80, and the coefficient of determination was 0.62. This study may provide some support for the snowmelt runoff forecast

郑文龙,2,3，都金康1,2,3*. 基于BP神经网络的玛纳斯河流域雪盖率预测[J]. 南京大学学报(自然科学版), 2015, 51(5): 1014–1021.

Zheng Wenlong1,2,3Du Jinkang1,2,3*. Forecasting snow cover fraction in Manasi River Basin based on BP neural network[J]. Journal of Nanjing University(Natural Sciences), 2015, 51(5): 1014–1021.

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