Inverse distance weight interpolation algorithm based on particle swarm local optimization

doi:10.11772/j.issn.1001-9081.2022010056

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 385-390.DOI: 10.11772/j.issn.1001-9081.2022010056

• Artificial intelligence • Previous Articles

Inverse distance weight interpolation algorithm based on particle swarm local optimization

Feng XIANG¹^,², Zhongzhi LI¹^,²(), Xi XIONG¹^,², Binyong LI¹^,²

^1.School of Cybersecurity，Chengdu University of Information Technology，Chengdu Sichuan 610255，China
^2.Advanced Cryptography and System Security Key Laboratory of Sichuan Province （Chengdu University of Information Technology），Chengdu Sichuan 610225，China

Received:2022-01-17 Revised:2022-04-06 Accepted:2022-04-11 Online:2022-04-21 Published:2023-02-10
Contact: Zhongzhi LI
About author:XIANG Feng， born in 1998， M. S. candidate. His research interests include machine learning， data analysis.
XIONG Xi， born in 1983， Ph. D.， associate professor. His research interests include artificial intelligence.
LI Binyong， born in 1982， Ph. D.， associate professor. His research interests include industrial intelligentization， data mining.
Supported by:
Sichuan Science and Technology Program(2021JDRC0046)

粒子群局部优化的反距离权重插值算法

向峰¹^,², 李中志¹^,²(), 熊熙¹^,², 李斌勇¹^,²

^1.成都信息工程大学网络空间安全学院, 成都 610255
^2.先进密码技术与系统安全四川省重点实验室(成都信息工程大学), 成都 610225

通讯作者: 李中志
作者简介:向峰（1998—），男，湖南怀化人，硕士研究生，主要研究方向：机器学习、数据分析
熊熙（1983—），男，四川成都人，副教授，博士，CCF会员，主要研究方向：人工智能
李斌勇（1982—），男，四川绵阳人，副教授，博士，CCF会员，主要研究方向：工业智能化、数据挖掘。
基金资助:
四川省科技计划项目(2021JDRC0046)

Abstract

Abstract:

The accuracy of Inverse Distance Weighting （IDW） will be affected by the selection of reference points and parameters. Aiming at the problem of ignoring local characteristics in multi-Parameter co-optimization Inverse Distance Weighting algorithm （PIDW）， an improved algorithm based on particle swarm local optimized IDW was proposed， namely Particle swarm Local optimization Inverse Distance Weight （PLIDW）. Firstly， the parameters of each sample point in the study area were optimized respectively， and the cross-validation method was used for evaluation， and the optimal set of parameters for each sample point was recorded. At the same time， in order to improve the query efficiency， a K-Dimensional Tree （KD-Tree） was used to save the spatial positions and optimal parameters. Finally， according to the spatial proximity， the nearest set of parameters was selected from KD-Tree to optimize IDW. Experimental results based on simulated data and real temperature dataset show that compared with PIDW， PLIDW has the accuracy on the real dataset improved by more than 4.18%. This shows that the low accuracy in some scenarios caused by ignoring local features in PIDW is improved by the proposed algorithm， and the adaptability is increased at the same time.

Key words: spatial interpolation, Inverse Distance Weight (IDW), anisotropy, multi-parameter optimization, Particle Swarm Optimization (PSO) algorithm

摘要：

参考点与参数的选取会对反距离权重（IDW）的精度产生影响。针对多参数协同优化反距离权重算法（PIDW）忽略局部特性的问题，提出一种利用粒子群对IDW进行局部优化的改进算法——PLIDW。首先，分别对研究区域中各个样本点的参数进行寻优，利用交叉验证方法进行评估，记录各自最优取值的一组参数；同时，为提高查询效率，使用K维树（KD-Tree）保存空间位置与最优参数；最后，根据空间邻近程度从K维树选取最近的一组参数优化IDW。基于模拟数据与真实的温度数据集上的实验结果表明，相较于PIDW，PLIDW在真实数据集上的准确度提高4.18%以上，改善了PIDW存在的因忽略局部特性导致部分场景准确度低的问题，适应能力更强。

关键词: 空间插值, 反距离权重, 各向异性, 多参数优化, 粒子群算法

CLC Number:

TP399

Feng XIANG, Zhongzhi LI, Xi XIONG, Binyong LI. Inverse distance weight interpolation algorithm based on particle swarm local optimization[J]. Journal of Computer Applications, 2023, 43(2): 385-390.

向峰, 李中志, 熊熙, 李斌勇. 粒子群局部优化的反距离权重插值算法[J]. 《计算机应用》唯一官方网站, 2023, 43(2): 385-390.

Figures/Tables 6

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算法	RMSE	MAPE	MAE	MAX
IDW	10.630	31.07	6.501	49.28
Kriging	11.050	33.24	7.053	46.96
SARNN	9.975	19.92	7.572	40.37
PIDW	10.220	29.93	6.289	45.91
PLIDW	9.722	28.22	6.054	46.54

算法	RMSE	MAPE	MAE	MAX
IDW	10.630	31.07	6.501	49.28
Kriging	11.050	33.24	7.053	46.96
SARNN	9.975	19.92	7.572	40.37
PIDW	10.220	29.93	6.289	45.91
PLIDW	9.722	28.22	6.054	46.54

算法	参考点数目
算法	20	40	60	80	100	120
IDW	5.604	5.295	4.348	5.814	5.424	5.144
Kriging	3.468	5.467	4.247	6.504	5.869	5.847
SARNN	3.045	3.627	4.012	6.129	5.895	4.670
PIDW	4.077	5.293	4.540	5.597	5.449	5.437
PLIDW	5.711	6.026	4.803	5.831	5.800	6.285

算法	参考点数目
算法	20	40	60	80	100	120
IDW	5.604	5.295	4.348	5.814	5.424	5.144
Kriging	3.468	5.467	4.247	6.504	5.869	5.847
SARNN	3.045	3.627	4.012	6.129	5.895	4.670
PIDW	4.077	5.293	4.540	5.597	5.449	5.437
PLIDW	5.711	6.026	4.803	5.831	5.800	6.285

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Inverse distance weight interpolation algorithm based on particle swarm local optimization

粒子群局部优化的反距离权重插值算法

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