Comparison of approaches for spatial interpolation of weather data on a specific date

Burengengwa, Gloria (2020-03)

Thesis (MSc)--Stellenbosch University, 2020.


ENGLISH ABSTRACT: This study compares four approaches to spatial interpolation of minimum and maximum temperature, and rainfall weather variables using data from 92 weather stations in the region of KwaZulu-Natal in South Africa. The approaches are Kriging with external drift (KED), Gaussian filter (GF), random forest (RF) and multilayer perceptron (MLP). The comparison was done against the background that the need for permanent gridded weather data for the region is important for agricultural and forest management. Also, there is little information regarding the suitability of methods for prediction in terms of performance variables for gridded data generation. The present research addresses these challenges by demonstrating the application of KED, GF, RF and MLP at a 1km2 spatial resolution accross three weather variables: minimum and maximum temperature, and rainfall to assess their performance. Four specific dates were selected to represent both dry and wet seasons for the years 2016 and 2017. The dates are 15th of January 2016 and of 2017 for the summer season, and 15th of July 2016 and of 2017 for the winter season respectively. Both years were considered because from available data, they are on the records as the driest (2016) and wettest (2017) in the region for the period 2008 to 2018. A cross-validation scheme was employed to assess the model performances and error evaluations were compared using RMSE, MAE and R2 measures. The results were found to be almost similar across the four methods except for the RF model that outperformed in the periods considered for both years. Particularly, RF performed with the lowest RMSE and MAE errors for minimum and maximum temperature for both 15th of July 2016 and of 2017 as against the other models. The performance of RF is explained by the method’s properties of being an ensemble technique. RF prediction follows from the principle of random selection of variables with high importance which allows for the decrease of uncertainty. The result of this research has importance for guiding decisions regarding forest management and climate driven businesses.

AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar.

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