Comparative Evaluation of Spatial Interpolation Methods for Estimation of Missing Meteorological Variables over Ethiopia
نویسندگان
چکیده
منابع مشابه
Performance evaluation of different estimation methods for missing rainfall data
There are numerous methods to estimate missing values of which some are used depending on the data type and regional climatic characteristics. In this research, part of the monthly precipitation data in Sarab synoptic station, east Azerbaijan province, Iran was randomly considered missing values. In order to study the effectiveness of various methods to estimate missing data, by seven classic s...
متن کاملEstimation of Missing Rainfall Data in Northeast Region of Thailand Using Spatial Interpolation Methods
Ground-based rainfall observations are the primary sources of precipitation data used in most developing countries. However, those observations are frequently damaged or incomplete, thus missing data is always a problem. This comparison study examines a number of spatial interpolation methods used to estimate missing monthly rainfall data in the northeast region of Thailand. The comparison was ...
متن کاملSpatial interpolation of daily meteorological data
E.G. Beek, 1991. Spatial interpolation of daily meteorological data; theoretical evaluation of available techniques. Wageningen (The Netherlands), DLO The Winand Staring Centre. Report 53.1.44 pp.; 13 Figs; 1 Table; 20 Refs. In agromcteorological crop yield models meteorological values at not observed points have to be obtained by means of interpolation techniques. In this study, interpolation ...
متن کاملKernel Methods for Missing Variables
We present methods for dealing with missing variables in the context of Gaussian Processes and Support Vector Machines. This solves an important problem which has largely been ignored by kernel methods: How to systematically deal with incomplete data? Our method can also be applied to problems with partially observed labels as well as to the transductive setting where we view the labels as miss...
متن کاملGeostatistical Approach for Spatial Interpolation of Meteorological Data.
Meteorological data are used in many studies, especially in planning, disaster management, water resources management, hydrology, agriculture and environment. Analyzing changes in meteorological variables is very important to understand a climate system and minimize the adverse effects of the climate changes. One of the main issues in meteorological analysis is the interpolation of spatial data...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Water Resource and Protection
سال: 2017
ISSN: 1945-3094,1945-3108
DOI: 10.4236/jwarp.2017.98063