Evaluating different Machine Learning Techniques for Spatial Interpolation of Environmental Data

Spatial interpolation is an important technique in environmental science, enabling the estimation of unmeasured values based on known data points. This is particularly important for understanding environmental phenomena such as air quality, temperature variations and pollutant levels, which are often collected at discrete locations. Traditional interpolation methods, such as Kriging and Inverse Distance Weighting (IDW), have been widely used; however, they may not always capture the complexities of spatial patterns effectively. With the advent of Machine Learning (ML), there is significant potential to improve interpolation accuracy. This essay evaluates various machine learning techniques, including Random Forest, Support Vector Regression (SVR) and Artificial Neural Networks (ANN), for their effectiveness in spatial interpolation of environmental data.