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• W4220744046 abstract "Spatial interpolation methods usually differ in their underlying mathematical concepts. Each has inherent advantages and disadvantages, and choosing a method should be based on the type of data to be analyzed. This paper, therefore, compares and evaluates the performances of well-established interpolation techniques that can be used to estimate monthly rainfall in Thailand. The approaches analyzed include inverse distance weighting (IDW), inverse exponential weighting (IEW), multiple linear regression (MLR), artificial neural networks (ANN), and ordinary kriging (OK) methods. In addition, a search of the nearest stations has also been conducted for some of the aforementioned schemes. A k-fold cross-validation is exploited to assess the efficiency of each method. Results show that ANN might be the least desirable choice as it underperformed, with the remaining methods being roughly comparable. Considering both accuracy and computational flexibility, the IEW approach with a restricted number of neighboring stations is recommended in this study." @default.
• W4220744046 created "2022-04-03" @default.
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• W4220744046 date "2022-01-27" @default.
• W4220744046 modified "2023-09-26" @default.
• W4220744046 title "Spatial interpolation methods for estimating monthly rainfall distribution in Thailand" @default.
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• W4220744046 doi "https://doi.org/10.1007/s00704-022-03927-7" @default.
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