两种不同模型对漂流适宜性等级预报的对比分析
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摘要
利用常规地面气象观测资料,对桃源河景区2015年1月1日—2016年12月31日2 a逐日漂流客流量与前一日气象要素进行了分析。BP网络和判别分析均具有良好的非线性函数拟合效果,分别利用这两种方法建立模型,并检验。结合日常经验挑选出主要影响漂流气象要素(日雨量、最高气温、最低气温、平均温度、平均相对湿度、最小相对湿度、08—20时降水量和风速),作为计算漂流等级预报的气象因子,建立漂流适宜性等级方程;同时为了降低暑假、周末和节假日对预报效果的影响,进行二次建模。结果发现:BP网络预报模型较好,预报等级正确率均高于40%,同时等级绝对值差为1的正确率均高于70%,正确率均高于判别分析法,预测的等级也更稳定;此外,暑假对漂流等级预报具有正面影响。
In this paper the 2-year daily rafting tourist amount and the previous day's a meteorological elements of Taoyuan River scenic area from January 1 st,2015 to December 31 st,2016 were analyzed by using conventional ground meteorological observation data.Both BP network and discriminant analysis methods have good nonlinear function fitting effects,and they were separately used and tested for establishing model.As a meteorological factor for calculating rafting grade prediction,combined with daily experience,the main affecting weather factors were selected(daily precipitation,highest temperature,lowest temperature,average temperature,average relative humidity,minimum relative humidity,08-20 precipitation and wind speed)for establishing the grade equation of rafting suitability.Meanwhile,secondary modelling was carried out in order to reduce the impact on the prediction effect from summer holidays,weekends and other statutory holidays.The result shows that BP network prediction model is better,for its prediction grade accuracy is higher than 40%,and the absolute value of 1 accuracy is higher than 70%,it is more accurate and stable than the discriminant analysis method.In addition,the summer holidays has a positive influence on rafting grade forecast.
In this paper the 2-year daily rafting tourist amount and the previous day's a meteorological elements of Taoyuan River scenic area from January 1 st,2015 to December 31 st,2016 were analyzed by using conventional ground meteorological observation data.Both BP network and discriminant analysis methods have good nonlinear function fitting effects,and they were separately used and tested for establishing model.As a meteorological factor for calculating rafting grade prediction,combined with daily experience,the main affecting weather factors were selected(daily precipitation,highest temperature,lowest temperature,average temperature,average relative humidity,minimum relative humidity,08-20 precipitation and wind speed)for establishing the grade equation of rafting suitability.Meanwhile,secondary modelling was carried out in order to reduce the impact on the prediction effect from summer holidays,weekends and other statutory holidays.The result shows that BP network prediction model is better,for its prediction grade accuracy is higher than 40%,and the absolute value of 1 accuracy is higher than 70%,it is more accurate and stable than the discriminant analysis method.In addition,the summer holidays has a positive influence on rafting grade forecast.
引文
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[4]吴龙,吴哲红,方庆文.安顺旅游舒适度分析[J].贵州气象,2009,33(增):69-70.
[5]阿布都克日木·阿巴司,努尔帕提曼·买买提热依木,帕提曼·阿不都艾尼,等.喀什地区夏玉米发育期与气象因子关系分析[J].沙漠与绿洲气象,2016,10(4):25-31.
[6]陶生才,潘婕,张磊,等.1971-2013年敦煌旅游气候舒适度分析与评价[J].沙漠与绿洲气象,2016,10(1):27-33.
[7]杨胜忠,陈少华.暴雨山洪对黔东南漂流旅游影响研究[J].贵州气象,2012,6(36):22-24.
[8]邓学军,刘建粤,白慧.杉木河旅游气候资源分析及应用[J].贵州气象,2005,3(29):18-20.
[9]彭洁,宗志平,黄小玉,等.湖南猛洞河漂流气候适宜性等级评价及预报方程的建立[J].气象,2011,6(37):771-776.
[10]向红琼,于飞.贵州山地高分辨率旅游气象舒适度时空分布特征[J].贵州气象,2010,3(34):4-6.
[11]邱粲,曹洁,林隆超,等.基于GIS的山东省旅游气候舒适度精细化评估[J].资源科学,2013,35(12):2501-2506.
[12]徐明时,邵玉平,王彦明.区域自动气象站在山洪灾害防治中的应用[J].黑龙江气象,2011,28(3):19.
[13]叶金印,李致家,刘静,等.山洪灾害气象风险预警指标确定方法研究[J].暴雨灾害,2016,35(1):36-30.
[14]程丽君,姚敏,高鸣远.上游降水及下游河闸对南京东山站水位的影响分析[J].水利信息化,2015(4):21-24.
[15]黄嘉佑.气象统计分析与预报方法[M].3版.北京:气象出版社,2007.
[16]王立德,李欣然,李培强,等.基于人工神经网络的综合负荷模型[J].电网技术,2008,32(16):59-65.
[17]王小川,史峰,郁磊,等.MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2014:40-44.