Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach

Seo, Youngmin; Kim, Sung-Won; Singh, Vijay P.

doi:10.1007/s11269-015-0935-9

Cited by 54 publications

(29 citation statements)

References 29 publications

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“…In addition, some scholars built hybrid interpolation algorithms by integrating the machine learning algorithm and geostatistics. For example, Seo et al [40] established a hybrid algorithm combining regression Kriging and neural network residual Kriging (RKNNRK). It can be seen from examples that the accuracy of RKNNRK is not only higher than RK and UK, but it is also higher than that of the common neural network and residual Kriging coupled algorithm (NNRK).…”

Section: Hybrid Interpolationmentioning

confidence: 99%

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Wang

et al. 2019

Water

102

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Rainfall is one of the most basic meteorological and hydrological elements. Quantitative rainfall estimation has always been a common concern in many fields of research and practice, such as meteorology, hydrology, and environment, as well as being one of the most important research hotspots in various fields nowadays. Due to the development of space observation technology and statistics, progress has been made in rainfall quantitative spatial estimation, which has continuously deepened our understanding of the water cycle across different space-time scales. In light of the information sources used in rainfall spatial estimation, this paper summarized the research progress in traditional spatial interpolation, remote sensing retrieval, atmospheric reanalysis rainfall, and multi-source rainfall merging since 2000. However, because of the extremely complex spatiotemporal variability and physical mechanism of rainfall, it is still quite challenging to obtain rainfall spatial distribution with high quality and resolution. Therefore, we present existing problems that require further exploration, including the improvement of interpolation and merging methods, the comprehensive evaluation of remote sensing, and the reanalysis of rainfall data and in-depth application of non-gauge based rainfall data.

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Section: Hybrid Interpolationmentioning

confidence: 99%

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Wang

et al. 2019

Water

102

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“…These methods include traditional statistical methods, machine learning methods, the hybrid methods of traditional statistical methods and geostatistical methods, and the hybrid methods of machine learning and geostatistical methods ( Table 1). These methods were applied or compared in various spatial predictive modeling studies [12,41,[47][48][49][50][51][52][53][54][55]. Of these methods, random forest (RF), hybrid method of RF and OK (RFOK), and hybrid method of RF and IDW (RFIDW) were among the most accurate methods in these applications.…”

Section: Spatial Predictive Methodsmentioning

confidence: 99%

A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

2019

Applied Sciences

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Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using spm, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences.

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“…In addition, Zhu and Huang (2007) proposed the highest elevation within 3 km of the site and the distance to Thousand-Islet Lake as a precipitation influencing factor [17]; Sun et al (2015) considered the surface roughness and river network density factor [18]; Bostan et al (2012) took the distance to the nearest coast, land cover and eco-region as precipitation interpolation auxiliary variables [19]; Seo et al (2015) included the distance to the summit of the Halla Mountain and the distance to the coastline into the calculation [20]; temperature, wind speed and other variables also have a certain degree of impact on the precipitation. Taking the various factors that affect precipitation into consideration is helpful to improve the interpolation precision, but one should not think that the more factor choices there are, the better the interpolation effect is.…”

Section: Introductionmentioning

confidence: 99%

Application of a Hybrid Interpolation Method Based on Support Vector Machine in the Precipitation Spatial Interpolation of Basins

Zhang

Liu

Wang³

et al. 2017

Water

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Abstract:In this paper, we applied the support vector machine (SVM) to the spatial interpolation of the multi-year average annual precipitation in the Three Gorges Region basin. By combining it with the inverse distance weighting and ordinary kriging method, we constructed the SVM residual inverse distance weighting, as well as the SVM residual kriging precipitation interpolation model and compared them with the inverse distance weighting, ordinary kriging, linear regression residual inverse distance weighting and linear regression residual kriging interpolation methods. The TRMM 3B43 V7 satellite precipitation information, which is processed by the latest revision algorithm, is used as the auxiliary variable for ground site precipitation interpolation along with latitude and elevation. Our results show that: (1) adding the TRMM 3B43 V7 satellite precipitation data as an auxiliary variable significantly improves the interpolation accuracy of the linear regression equation and SVM model; (2) the support vector machine hybrid interpolation method obtains superior interpolation results compared to the inverse distance weighting method, ordinary kriging method and linear regression hybrid interpolation method; (3) the interpolation accuracy of the SVM hybrid interpolation method depends on the SVM fitting degree, so we should choose a suitable fitting accuracy rather than the highest fitting accuracy; (4) the linear regression equation has a greater degree of dependency on the TRMM data than the SVM. The SVM accepts the TRMM data information while better maintaining its independence, taking into account that the TRMM data linear regression and linear regression hybrid interpolation method are not suitable for TRMM data evaluation.

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Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach

Cited by 54 publications

References 29 publications

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

Application of a Hybrid Interpolation Method Based on Support Vector Machine in the Precipitation Spatial Interpolation of Basins

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