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Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin China

机译:黑河流域中游生态水文无线传感器网络的混合优化设计

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摘要

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.
机译:中国黑河流域中游的生态水文无线传感器网络(EHWSN)旨在捕获时空变化并估算地面真实性,以验证遥感生产。但是,没有有关目标变量的可用先验信息。为了满足这两个要求,开发了一种基于混合模型的,没有任何空间自相关假设的采样方法,以基于地统计学来优化EHWSN节点的分布。该混合模型包含两个子标准:一个用于变异函数建模以表示变异性,另一个用于改进空间预测以评估遥感生产。通过使用无条件地统计随机模拟生成的15种类型的模拟场,从代表性,变异函数建模和空间准确性方面验证了优化的EHWSN的合理性。抽样设计具有良好的代表性。样本估计的变异函数相对于真实变异函数的平均误差小于3%。然后,预测多个尺度的场。随着规模的增加,在块大小超过240 m时,估计字段与模拟字段具有更高的相似性。验证证明,当我们不知道优化变量的特征时,这种混合采样方法对于两个目标都是有效的。

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