共查询到20条相似文献，搜索用时 15 毫秒
1.
Comparative analysis of regression and artificial neural network models for wind speed prediction 总被引：1，自引：1，他引：0
In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods. 相似文献
2.
Prediction of soil temperature using regression and artificial neural network models 总被引：3，自引：0，他引：3
Mehmet Bilgili 《Meteorology and Atmospheric Physics》2010,110(1-2):59-70
In this study, monthly soil temperature was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. The soil temperature and other meteorological parameters, which have been taken from Adana meteorological station, were observed between the years of 2000 and 2007 by the Turkish State Meteorological Service (TSMS). The soil temperatures were measured at depths of 5, 10, 20, 50 and 100 cm below the ground level. A three-layer feed-forward ANN structure was constructed and a back-propagation algorithm was used for the training of ANNs. In order to get a successful simulation, the correlation coefficients between all of the meteorological variables (soil temperature, atmospheric temperature, atmospheric pressure, relative humidity, wind speed, rainfall, global solar radiation and sunshine duration) were calculated taking them two by two. First, all independent variables were split into two time periods such as cold and warm seasons. They were added to the enter regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and they were also used in the input layer of the ANN method. Results of these methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods. 相似文献
3.
M. A. Ghorbani R. Khatibi B. Hosseini M. Bilgili 《Theoretical and Applied Climatology》2013,114(1-2):107-114
In traditional artificial neural networks (ANN) models, the relative importance of the individual meteorological input variables is often overlooked. A case study is presented in this paper to model monthly wind speed values using meteorological data (air pressure, air temperature, relative humidity, and precipitation), where the study also includes an estimate of the relative importance of these variables. Recorded monthly mean data are available at a gauging site in Tabriz, Azerbaijan, Iran, for the period from 2000 to 2005, gauged in the city at the outskirt of alluvial funneling mountains with an established microclimatic conditions and a diurnal wind regime. This provides a sufficiently severe test for the ANN model with a good predictive capability of 1 year of lead time but without any direct approach to refer the predicted results to local microclimatic conditions. A method is used in this paper to calculate the relative importance of each meteorological input parameters affecting wind speed, showing that air pressure and precipitation are the most and least influential parameters with approximate values of 40 and 10 %, respectively. This gained knowledge corresponds to the local knowledge of the microclimatic and geomorphologic conditions surrounding Tabriz. 相似文献
4.
Least squares support vector machine for short-term prediction of meteorological time series 总被引：3，自引：1，他引：2
The prediction of meteorological time series plays very important role in several fields. In this paper, an application of least squares support vector machine (LS-SVM) for short-term prediction of meteorological time series (e.g. solar irradiation, air temperature, relative humidity, wind speed, wind direction and pressure) is presented. In order to check the generalization capability of the LS-SVM approach, a K-fold cross-validation and Kolmogorov–Smirnov test have been carried out. A comparison between LS-SVM and different artificial neural network (ANN) architectures (recurrent neural network, multi-layered perceptron, radial basis function and probabilistic neural network) is presented and discussed. The comparison showed that the LS-SVM produced significantly better results than ANN architectures. It also indicates that LS-SVM provides promising results for short-term prediction of meteorological data. 相似文献
5.
辽宁省现有测站春播期土壤相对湿度数据存在不连续性及长时间序列缺失问题。以海城站为例,分析现有土壤相对湿度（0-20 cm）与气象因子及临近站点土壤相对湿度的相关关系,构建海城春播期土壤相对湿度统计回归模型,模拟缺测时段春播期土壤相对湿度。进而以此方法重建辽宁省20个观测站1981-2012年春播期土壤相对湿度月尺度数据。结果表明：海城土壤相对湿度与降水量和秋季封冻雨关联较大,相关系数分别超过0.60和0.30,与同期临近站点本溪站土壤相对湿度相关性也超过0.40,依据该3要素构建的4月和5月土壤相湿度统计回归模型复相关系数R2分别达0.79和0.77,模拟结果与实测资料平均相对误差为2.6 %,模拟效果较好;对辽宁省其他数据缺失站点构建的回归模型复相关系数均高于0.50,模型拟合精度优于85 %,拟合值和实测值平均相对误差基本控制在15 %以内,较好完成辽宁省20个测站1981-2012年春播期土壤相对湿度月尺度数据重建。 相似文献
6.
Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region 总被引：5，自引：0，他引：5
Hossein Tabari Ali-Akbar Sabziparvar Mohammad Ahmadi 《Meteorology and Atmospheric Physics》2011,110(3-4):135-142
Soil temperature (T S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T a), solar radiation (R S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T a, RH, R S and P were reasonably correlated with T S at various depths, but the most effective parameters influencing T S at different depths were T a and RH. 相似文献
7.
夏季不同下垫面气象要素的对比分析 总被引：5，自引：4，他引：5
对扎龙湿地生态气象站和齐齐哈尔自动气象站2004年6~8月的逐时气温、风速、相对湿度等观测资料进行统计分析,找出了不同下垫面的两站之间不同时次各气象要素的差异,指出两站风速、气温、露点温度、相对湿度、地面温度等的月平均值均存在差异,变化幅度及极值出现时间各有不同;下垫面差异只起到加强或缓和气象要素日变化幅度的作用。所得结果有助于深入了解扎龙湿地的天气变化规律,对未来扎龙湿地气象要素的精细预报以及湿地资源的开发、保护和管理有一定的参考意义。 相似文献
8.
利用MODIS资料监测京津冀地区近地面PM2.5方法研究 总被引：7，自引：0，他引：7
为建立京津冀地区冬季近地面细颗粒物浓度监测方法模型,利用气象模式资料对2013年1-3月MODIS的AOD二级深蓝算法产品进行湿度和垂直订正,与同期观测的地面细颗粒物PM2.5资料进行相关分析。结果表明：AQUA的MODIS深蓝算法AOD产品更适用于建立冬季AOD-PM2.5遥感监测模型,其R2为0.33;以气象模式资料中边界层高度代替气溶胶标高对MODIS的AOD进行垂直订正,并结合IMPROVE观测的气溶胶吸湿增长特征构建分区湿度订正方法,可以提高AOD-PM2.5模型结果的精度,建立较为理想的京津冀地区冬季遥感反演综合模型,模型结果与地面监测结果R2达0.5以上。根据建立的模型计算了2013年1-3月的京津冀地区PM2.5月平均浓度,京津冀地区1月的PM2.5浓度较高,南部大部分地区空气质量已经达到重度污染水平。 相似文献
9.
利用2016-2018年库尔勒气象站迁站前后基本气象要素的观测资料进行对比分析,结果显示：(1)平均气温、平均最低气温年、月值均是新站低于旧站,年值分别低2.1℃和4.1℃,年平均最高气温持平；春季气温差值变化相对较小,夏、秋、冬季气温差值变化相对偏大。(2)各月相对湿度新站大于旧站,各季相对湿度差值夏季最大,年平均相对湿度新站比旧站高11%。(3)平均气压新站高于旧站,年平均气压差值为3.2pha。各季差值冬季最大,(4)平均风速新站比旧站偏大0.1m/s,春季、夏季风速大于其他季节；最大风速新站比旧站偏大1.3-6.2m/s；主导风向由ENE转为E。(5)年平均气温、最低气温、平均湿度和年平均气压,迁站前后资料有显著差异,年平均最高气温、平均风速无显著差异。(6)测站周围环境、海拔高度、下垫面、地形等因素是造成新旧站气象要素差异的主要原因。 相似文献
10.
11.
成都地区秋、冬季GPS可降水量的时空分析 总被引：6，自引：4，他引：2
利用成都地区5个测站地基GPS2007年9月-2008年2月的观测数据,解算出1 min间隔的天顶总延迟,结合自动气象站资料计算出30 min间隔的大气可降水量(GPS-PWV).对月平均的GPS-PWV分析表明:秋、冬季变化趋势从9月开始下降,1月达到最小值,2月又逐渐上升.在大气环流相同的情况下,地理位置相近的站,海拔高的地区大气中的水汽量比海拔低的地区要少,且变化较大;海拔高度相近的站,大气中的水汽含量由南向北减少.日合成分析显示:在静稳天气下,日变化特征显著,具有双峰型特征:白天峰值与气温的最大值相对应;夜间峰值与降水量的峰值相对应;GPS-PWV与地面空气相对湿度白天呈负相关,夜间呈正相关. 相似文献
12.
Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m?2?d?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m?2?d?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area. 相似文献
13.
影响莫高窟小气候的环境因子对比分析 总被引：2，自引：0，他引：2
应用不同时空尺度对莫高窟的小气候进行对比分析,发现莫高窟的地形地貌、水系统、植被等环境因子对其小气候有重要影响。莫高窟的小地形结构和水系统对其风速、湿度、太阳辐射等有显著影响。2005年4—12月窟前与窟顶月平均相比,太阳辐射总量降低411．21MJ／m^2,地表温度降低2．7℃,相对湿度增大9％,风速降低3．7m／s。窟前月较差小于戈壁0．9℃。但对于气温而言,主要受较大尺度因素的控制,窟前、窟顶、戈壁均处于同一温度层。2004年莫高窟日较差比敦煌小4．8℃。莫高窟的环境因子使莫高窟周边形成了更为稳定的小气候,非常有利于文物保护,这也是千年莫高窟保存至今的一个重要原因。 相似文献
14.
A model of the evolution of the nocturnal stable boundary layer height, based on the heat conservation equation for a turbulent flow, is presented. This model is valid for nights with weak winds and little cloudiness in rural areas. The model includes an expression of vertical profile of potential temperature within the boundary layer, which is obtained using micrometeorological information from Prairie Grass, Wangara and O'Neill Projects. The expression turned out to be a second-grade polynomial of the dimensionless height of the nocturnal stable boundary layer. The resulting model is a function of the Monin–Obukhov length, the surface potential temperature of air and the roughness length. This model was satisfactorily compared with micrometeorological data. It was applied at three stations of Argentina, using surface hourly meteorological information. From the results that were obtained, the monthly average values of the stable boundary layer thickness were analysed. The maximum monthly average values occur during the cold season and the minimum ones take place during the hot season. It was observed that the monthly average thickness increases with latitude. 相似文献
15.
Important meteorological variables for statistical long-term air quality prediction in eastern China
Weather is an important factor for air quality. While there have been increasing attentions to long-term (monthly and seasonal) air pollution such as regional hazes from land-clearing fires during El Niño, the weather-air quality relationships are much less understood at long-term than short-term (daily and weekly) scales. This study is aimed to fill this gap through analyzing correlations between meteorological variables and air quality at various timescales. A regional correlation scale was defined to measure the longest time with significant correlations at a substantial large number of sites. The air quality index (API) and five meteorological variables during 2001–2012 at 40 eastern China sites were used. The results indicate that the API is correlated to precipitation negatively and air temperature positively across eastern China, and to wind, relative humidity and air pressure with spatially varied signs. The major areas with significant correlations vary with meteorological variables. The correlations are significant not only at short-term but also at long-term scales, and the important variables are different between the two types of scales. The concurrent regional correlation scales reach seasonal at p < 0.05 and monthly at p < 0.001 for wind speed and monthly at p < 0.01 for air temperature and relative humidity. Precipitation, which was found to be the most important variable for short-term air quality conditions, and air pressure are not important for long-term air quality. The lagged correlations are much smaller in magnitude than the concurrent correlations and their regional correction scales are at long term only for wind speed and relative humidity. It is concluded that wind speed should be considered as a primary predictor for statistical prediction of long-term air quality in a large region over eastern China. Relative humidity and temperature are also useful predictors but at less significant levels. 相似文献
16.
Multiscale evolution of surface air temperature in the arid region of Northwest China and its linkages to ocean oscillations 总被引：1，自引：0，他引：1
Chen Zhongsheng Chen Yaning Bai Ling Xu Jianhua 《Theoretical and Applied Climatology》2017,130(3-4):945-958
Reference crop evapotranspiration (ETo) is one of the most important links in hydrologic circulation and greatly affects regional agricultural production and water resource management. Its variation has drawn more and more attention in the context of global warming. We used the Penman-Monteith method of the Food and Agriculture Organization, based on meteorological factors such as air temperature, sunshine duration, wind speed, and relative humidity to calculate the ETo over 46 meteorological stations located in the Yangtze River Delta, eastern China, from 1957 to 2014. The spatial distributions and temporal trends in ETo were analyzed based on the modified Mann-Kendall trend test and linear regression method, while ArcGIS software was employed to produce the distribution maps. The multiple stepwise regression method was applied in the analysis of the meteorological variable time series to identify the causes of any observed trends in ETo. The results indicated that annual ETo showed an obvious spatial pattern of higher values in the north than in the south. Annual increasing trends were found at 34 meteorological stations (73.91 % of the total), which were mainly located in the southeast. Among them, 12 (26.09 % of the total) stations showed significant trends. We saw a dominance of increasing trends in the monthly ETo except for January, February, and August. The high value zone of monthly ETo appeared in the northwest from February to June, mid-south area from July to August, and southeast coastal area from September to January. The research period was divided into two stages—stage I (1957–1989) and stage II (1990–2014)—to investigate the long-term temporal ETo variation. In stage I, almost 85 % of the total stations experienced decreasing trends, while more than half of the meteorological stations showed significant increasing trends in annual ETo during stage II except in February and September. Relative humidity, wind speed, and sunshine duration were identified as the most dominant meteorological variables influencing annual ETo changes. The results are expected to assist water resource managers and policy makers in making better planning decisions in the research region. 相似文献
17.
利用江苏省24个台站的1981-2012年的气温、湿度和降水的月平均观测资料,分别计算了每对台站之间的3个气候要素的结构关系和相关函数,用曲线回归分析了结构函数与台站距离的关系,用线性回归分析了相关函数与台站距离的关系,分析与评估江苏省气象台站网密度.通过分析结果发现,各个气象要素的结构函数和相关函数在江苏整个地区不满足各项同性和均匀性,江苏地区的气象台站网设计要根据气候要素进行分区设计. 相似文献
18.
The prediction of Indian summer monsoon rainfall (ISMR) on a seasonal time scales has been attempted by various research
groups using different techniques including artificial neural networks. The prediction of ISMR on monthly and seasonal time
scales is not only scientifically challenging but is also important for planning and devising agricultural strategies. This
article describes the artificial neural network (ANN) technique with error- back-propagation algorithm to provide prediction
(hindcast) of ISMR on monthly and seasonal time scales. The ANN technique is applied to the five time series of June, July,
August, September monthly means and seasonal mean (June + July + August + September) rainfall from 1871 to 1994 based on Parthasarathy
data set. The previous five years values from all the five time-series were used to train the ANN to predict for the next
year. The details of the models used are discussed. Various statistics are calculated to examine the performance of the models
and it is found that the models could be used as a forecasting tool on seasonal and monthly time scales. It is observed by
various researchers that with the passage of time the relationships between various predictors and Indian monsoon are changing,
leading to changes in monsoon predictability. This issue is discussed and it is found that the monsoon system inherently has
a decadal scale variation in predictability.
Received: 13 March 1999 / Accepted: 31 August 1999 相似文献
19.
Antonio Geraldo Ferreira Emilio Soria-Olivas Antonio Jos�� Serrano L��pez Ernesto Lopez-Baeza 《Theoretical and Applied Climatology》2011,106(1-2):263-279
This study describes the results of artificial neural network (ANN) models to estimate net radiation (R n), at surface. Three ANN models were developed based on meteorological data such as wind velocity and direction, surface and air temperature, relative humidity, and soil moisture and temperature. A comparison has been made between the R n estimates provided by the neural models and two linear models (LM) that need solar incoming shortwave radiation measurements as input parameter. Both ANN and LM results were tested against in situ measured R n. For the LM ones, the estimations showed a root mean square error (RMSE) between 34.10 and 39.48?W?m?2 and correlation coefficient (R 2) between 0.96 and 0.97 considering both the developing and the testing phases of calculations. The estimates obtained by the ANN models showed RMSEs between 6.54 and 48.75?W?m?2 and R 2 between 0.92 and 0.98 considering both the training and the testing phases. The ANN estimates are shown to be similar or even better, in some cases, than those given by the LMs. According to the authors?? knowledge, the use of ANNs to estimate R n has not been discussed earlier, and based on the results obtained, it represents a formidable potential tool for R n prediction using commonly measured meteorological parameters. 相似文献
20.
本文利用贵阳市8区县国家级地面观测站1987-2016年的逐日地面观测资料,运用统计分析、相关分析、通径分析等方法来分析了贵阳地区的气温、相对湿度、风速等气象要素的气候特征,并对人体舒适度指数的时空分布特征进行了分析,探讨了各气象要素与人体舒适度指数之间的相关关系。结果表明：(1)贵阳地区的气温分布存在南北差异,南部最高,中部次之,北部最低;贵阳各地每月的平均相对湿度都很大,均在75%以上;月平均风速在1.5-2.5m/s之间,空间分布差异不大。(2)4月到10月,来贵阳各地旅游都较为舒适,尤其是在夏季(6-9月),最为舒适。从空间分布来看,人体舒适度指数分布为南部＞中部＞北部。(3)人体舒适度指数和气温为明显的正相关关系,与相对湿度、风速为负相关关系。其中气温对人体舒适度指数的影响最为显著。 相似文献