Comparison of Quantitative Precipitation Estimation Using Spatial Interpolation methods in Bukhan River Basin

2020 ◽  
Author(s):  
Sanghoo Yoon ◽  
Junseok Kim ◽  
Taeyong Kwon
Keyword(s):  
Spatial Interpolation ◽  
Mean Squared Error ◽  
Additive Model ◽  
Absolute Error ◽  
Interpolation Methods ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Weight Method ◽  
Inverse Distance ◽  
Spatial Interpolation Methods

<p>Quantitative precipitation estimation is needed to reduce damages from weather disasters such as torrential rain. This study is dealt with estimates of the quantitative precipitation using multiple spatial interpolation methods and compares the results. Inverse distance weight method and k-nearest neighborhood algorithm were considered as a deterministic approach and the general additive model and kriging methods were used as a stochastic approach. To evaluate the prediction performance, leave-one-out cross-validation was performed with the root mean squared error (RMSE), mean absolute error (MAE), bias, and correlation coefficient. The research data were rain gauged and radar data in the Bukhan river, which were collected from May 2018 to August 2019. The results showed that the inverse distance weight method reflected the spatial rainfall characteristics well. However, caution is needed because the best models vary depending on the pattern of rainfall in the sense of RMSE.</p><p>*This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD(No. 2018-Tech-20)</p>

scholarly journals Spatial interpolation methods for estimating monthly rainfall distribution in Thailand

2021 ◽  
Author(s):  
Nawinda Chutsagulprom ◽  
Kuntalee Chaisee ◽  
Ben Wongsaijai ◽  
Papangkorn Inkeaw ◽  
Chalump Oonariya
Keyword(s):  
Spatial Interpolation ◽  
Cross Validation ◽  
Monthly Rainfall ◽  
Rainfall Distribution ◽  
Mathematical Concepts ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Fold Cross Validation ◽  
Spatial Interpolation Methods

Abstract Spatial interpolation methods usually differ in their underlying mathematical concepts, each with inherent advantages and drawbacks depending on the properties of data. This paper, therefore, aims to compare and evaluate the performances of well-established interpolation techniques for estimating monthly rainfall data in Thailand. The selected methods include the inverse distance-based method, multiple linear regression (MLR), artificial neural networks (ANN), and ordinary kriging (OK). The technique of searching nearest stations is additionally imposed for some aforementioned schemes. The k -fold cross-validation method is exploited to assess the efficiency of each method, then the metric scores, RMSE, and MAE are used for comparisons. The results suggest the ANN might be the least favorite as it underperforms in many folds. While the OK method provides the most accurate prediction, the inverse distance weighting (IDW), particularly inverse exponential weighting (IEW), and MLR are considerably comparative. Overall, IEW is plausible for monthly rainfall estimation of Thailand because it is less computationally expensive than the OK and its flexible computation.

scholarly journals AN IMPROVED TEMPERATURE SPATIAL INTERPOLATION METHOD FOR SPACEBORNE LIDAR ATMOSPHERIC CORRECTION

2021 ◽  
Vol V-4-2021 ◽  
pp. 83-88
Author(s):  
M. Zhou ◽  
K. Li ◽  
M. Pan ◽  
J. Chen ◽  
C. Li ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Interpolation Method ◽  
Meteorological Parameter ◽  
Atmospheric Correction ◽  
Absolute Error ◽  
Lapse Rate ◽  
Interpolation Methods ◽  
Spatial Interpolation Method ◽  
Meteorological Elements ◽  
Inverse Distance

Abstract. As one of the most important meteorological elements, temperature is an indispensable meteorological parameter for the atmospheric correction of spaceborne LiDAR ranging. Given a limited number of surface meteorological observation stations, the temperature values for all region of LiDAR observation need to be interpolated using appropriate spatial interpolation methods. In this paper, based on the monthly surface observation values in individual years (1981–2010) of Sichuan province observation stations, we firstly analyze the effects of three common interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). To solve the problem of low interpolation accuracy in severely undulating terrain area, an improved gradient distance inverse square method based on the adiabatic lapse rate (GIDS-ALR) is proposed. The experimental results show that the GIDS-ALR has an obvious improvement in the effect of severely undulating terrain, where the absolute error has been improved by more than 43% in average. Additionally, the temperature-interpolated MAE is reduced by more than 30%. The effectiveness and applicability of the proposed method is verified in this paper.

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