针对传统电磁频谱地图构建方法感知节点分布不均匀、监测数据存在异常值等问题,将基于密度的噪声应用空间聚类(Density-based Spatial Clustering of Applications with Noise,DBSCAN)算法与反距离加权(Inverse Distance Weighting,IDW...针对传统电磁频谱地图构建方法感知节点分布不均匀、监测数据存在异常值等问题,将基于密度的噪声应用空间聚类(Density-based Spatial Clustering of Applications with Noise,DBSCAN)算法与反距离加权(Inverse Distance Weighting,IDW)算法相结合,提出一种城市环境电磁频谱地图构建方法。该算法首先通过DBSCAN减轻极端值的影响,并分离核心点、边界点和噪声点,将每个核心点的局部密度作为权重,计算簇的加权中心点。其次,运用IDW对聚类簇的加权中心点进行插值估计,以显著减少需要进行插值的数据点数量,从而构建精度更高的电磁频谱地图。仿真结果表明:与IDW算法和反障碍距离加权算法相比,所提算法重构得到的电磁频谱地图的平均绝对误差和归一化均方误差分别降低了10%和23%以上。展开更多
Rainfall is a significant portion of hydrologic data. Rainfall records, however, are often incomplete due to several factors. In this study, the inverse distance weighting (IDW) method integrated with GIS is used to e...Rainfall is a significant portion of hydrologic data. Rainfall records, however, are often incomplete due to several factors. In this study, the inverse distance weighting (IDW) method integrated with GIS is used to estimate the rainfall distribution in Duhok Governorate. A total of 25 rain fall stations and rainfall data between 2000 and 2010 were used, where 6 rainfall stations were used for cross-validation. In addition, the relationship between interpolation accuracy and two critical parameters of IDW (Power α value, and a radius of influence) was evaluated. Also, the rainfall distribution of Duhok Governorate was classified. As an output of this study and in most cases, the optimal parameters for IDW in interpolating rainfall data must have a radius of influence up to (15 - 60 km). However, the optimal α values varied between 1 and 5. Based on the results of this study, we concluded that the IDW is an appropriate method of spatial interpolation to predict the probable rainfall data in Duhok Governorate using α = 1 and search radius = 105 km for all the 25 rainfall stations.展开更多
文摘针对传统电磁频谱地图构建方法感知节点分布不均匀、监测数据存在异常值等问题,将基于密度的噪声应用空间聚类(Density-based Spatial Clustering of Applications with Noise,DBSCAN)算法与反距离加权(Inverse Distance Weighting,IDW)算法相结合,提出一种城市环境电磁频谱地图构建方法。该算法首先通过DBSCAN减轻极端值的影响,并分离核心点、边界点和噪声点,将每个核心点的局部密度作为权重,计算簇的加权中心点。其次,运用IDW对聚类簇的加权中心点进行插值估计,以显著减少需要进行插值的数据点数量,从而构建精度更高的电磁频谱地图。仿真结果表明:与IDW算法和反障碍距离加权算法相比,所提算法重构得到的电磁频谱地图的平均绝对误差和归一化均方误差分别降低了10%和23%以上。
文摘Rainfall is a significant portion of hydrologic data. Rainfall records, however, are often incomplete due to several factors. In this study, the inverse distance weighting (IDW) method integrated with GIS is used to estimate the rainfall distribution in Duhok Governorate. A total of 25 rain fall stations and rainfall data between 2000 and 2010 were used, where 6 rainfall stations were used for cross-validation. In addition, the relationship between interpolation accuracy and two critical parameters of IDW (Power α value, and a radius of influence) was evaluated. Also, the rainfall distribution of Duhok Governorate was classified. As an output of this study and in most cases, the optimal parameters for IDW in interpolating rainfall data must have a radius of influence up to (15 - 60 km). However, the optimal α values varied between 1 and 5. Based on the results of this study, we concluded that the IDW is an appropriate method of spatial interpolation to predict the probable rainfall data in Duhok Governorate using α = 1 and search radius = 105 km for all the 25 rainfall stations.
