Abstract
Accurate rainfall data are essential for environmental applications in the actual assessment of the geographical distribution of rainfall. Interpolation methods are usually applied to monitor the spatial distribution of the rainfall data. There are many spatial interpolation methods, but none of them can achieve in all cases the best results. In this study, three different interpolation methods were investigated with regard to their suitability for producing a spatial rainfall distribution. Rainfall data from 14 meteorological stations were spatially interpolated using three common interpolation techniques: inverse distance weighting (IDW), ordinary kriging (OK), and kernel smoothing (KS) were compared and assessed against station rainfall data and modeled rainfall. Cross-validation was applied to evaluate the accuracy of interpolation methods in terms of the root-mean-square error (RMSE). The best results were obtained by the lowest RMSE for interpolating the precipitation (RMSE) = 100.86542, while the inverse distance weighting (IDW) performed the worst, and are least efficient with the largest RMSE=103.43; in addition, the kernel smoothing with the least minimum (-) and maximum (+) error is -92.38 mm and 313.33 mm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
