引用本文: 王旭, 林征, 张志, 李丹. 基于GWR模型的北极滨海平原融冻湖表面温度空间分布模拟[J]. 武汉大学学报 ( 信息科学版), 2016, 41(7): 918-924. WANG Xu, LIN Zheng, ZHANG Zhi, LI Dan. Modelling the Spatial Distribution of Lake Surface Water Temperature of the Thaw Lakes in Arctic Coastal Plain Using Geographically Weighted Regression Model[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 918-924.
 Citation: WANG Xu, LIN Zheng, ZHANG Zhi, LI Dan. Modelling the Spatial Distribution of Lake Surface Water Temperature of the Thaw Lakes in Arctic Coastal Plain Using Geographically Weighted Regression Model[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 918-924. ## Modelling the Spatial Distribution of Lake Surface Water Temperature of the Thaw Lakes in Arctic Coastal Plain Using Geographically Weighted Regression Model

• 摘要: 通过分析北极滨海平原融冻湖泊形态和空间特征与湖泊表面温度之间的相关性,选取湖泊面积、形态紧凑系数、平均深度、与楚科齐海岸线距离,与波弗特海岸线距离、纬度等6个影响因素为参数,分别利用普通最小二乘线性回归(OLS)法和地理加权回归(GWR)法构建湖泊表面温度的空间分布模型,并采用主成分分析法消除变量共线性以降低模型估计误差方差。研究结果表明,与OLS模型相比,GWR模型显著提高了模型拟合度(确定系数R2由0.648增至0.752)和精度(平均绝对误差从 0.47 K降至0.38 K;均方根误差从0.62 K降至0.44 K),能更好地模拟融冻湖泊表面温度的空间分布,可为极地地区区域性气候变化的研究提供更为可靠的多因素预测模型和统计解释。

Abstract: A Sub-region of the Arctic coastal plain of Alaska was divided according to latitude and distance from coastline, and were prepared for the selection of spatial impact factors that influence the average lake surface water temperature (LSWT). After analyzing the relationship between each factor with LSWT by isolating the other factors, the factors would be recalculated via logarithm or exponent transformation in order to satisfy a linear relationship if the relationship is nonlinear. The most related factors including lake area, compactness index, mean depth, the distance to Chukchi Sea, the distance to Beaufort Sea and latitude were used to construct the LSWT spatial distribution model. To decrease the spatial non-stationarity of the models, the principal component analysis was applied to eliminate the effect of multicollinearity among variables. Then the LSWT spatial distribution models were constructed by ordinary least squares regression and geographically weighted regression method, respectively. The validation results show that the accuracy of geographically weighted regression model improved compare with ordinary least squares regression model. The coefficient of determination of the geographically weighted Regression model, R-square, is promoted from 0.615 to 0.752. And compared with the OLS model. The MAE and RMSE of GWR model decreased from 0.48 to 0.38 and from 0.65 to 0.44, respectively. It demonstrates that the improved GWR model can moderately depict the spatial distribution of thawed lake surface water temperature on the arctic coastal plain of Alaska. / 下载:  全尺寸图片 幻灯片
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