Flood Prediction Using Inverse Distance Weighted Interpolation of K-Nearest Neighbor Points

2021 ◽  
Author(s):  
Satria Nusa Paradilaga ◽  
Margaretha Sulistyoningsih ◽  
Rosbintarti Kartika Lestari ◽  
Agatha Padma Laksitaningtyas
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Weighted Interpolation ◽  
Flood Prediction ◽  
Inverse Distance Weighted ◽  
Distance Weighted ◽  
Inverse Distance Weighted Interpolation ◽  
Inverse Distance

scholarly journals Interpolation of Daily Temperature in Finland

2003 ◽  
Vol 34 (5) ◽  
pp. 413-426 ◽  
Author(s):  
Antti Taskinen ◽  
Hannu Sirviö ◽  
Bertel Vehviläinen
Keyword(s):  
Daily Temperature ◽  
Alternative Methods ◽  
Weighted Interpolation ◽  
Inverse Distance Weighted ◽  
Distance Weighted ◽  
Inverse Distance Weighted Interpolation ◽  
Temperature Interpolation ◽  
Forecasting System ◽  
The Difference ◽  
Inverse Distance

The present approach for daily temperature interpolation of the Watershed Simulation and Forecasting System of the Finnish Environment Institute is based on the inverse distance weighted interpolation. In order to improve the calculation, three alternative methods were tested: 1) modified inverse distance weighted model, 2) regression with dummy variables for taking into account time and 3) regression equation calibrated for each day. The regression model calibrated for each day proved to be the most promising model. By average, the difference between the accuracy of it and the inverse distance weighted methods wasn't big but some indication was found that in single cases it can make a difference. The estimated parameters were found to have realistic physical meanings.

scholarly journals Denoising performance analysis of adaptive decision based inverse distance weighted interpolation (DBIDWI) algorithm for salt and pepper noise

2019 ◽  
Vol 15 (2) ◽  
pp. 804
Author(s):  
Vorapoj Patanavijit
Keyword(s):  
Impulsive Noise ◽  
Superior Performance ◽  
Range Image ◽  
Weighted Interpolation ◽  
Inverse Distance Weighted ◽  
Research Article ◽  
Distance Weighted ◽  
Inverse Distance Weighted Interpolation ◽  
Salt And Pepper ◽  
Inverse Distance

<p>Due to its superior performance for denoising an image, which is contaminated by impulsive noise, an adaptive decision based inverse distance weighted interpolation (DBIDWI) algorithm is one of the most dominant and successful denoising algorithm, which is recently proposed in 2017, however this DBIDWI algorithm is not desired for denoising the full dynamic intensity range image, which is comprised of min or max intensity. Consequently, the research article aims to study the performance and its limitation of the DBIDWI algorithm when the DBIDWI algorithm is performed in both general images and the images, which are comprised of min or max intensity. In this simulation experiments, six noisy images (Lena, Mobile, Pepper, Pentagon, Girl and Resolution) under salt&amp;pepper noise are used to evaluate the performance and its limitation of the DBIDWI algorithm in denoised image quality (PSNR) perspective.</p>

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