Geo-physical parameter forecasting on imagery{based data sets using machine learning techniques

Abstract

This research objectively investigates the e ectiveness of machine learning (ML) tools towards predicting several geo-physical parameters. This is based on a large number of studies that have reported high levels of prediction success using ML in the eld. Therefore, several widely used ML tools coupled with a number of di erent feature sets are used to predict six geophysical parameters namely rainfall, groundwater, evapora- tion, humidity, temperature, and wind. The results of the research indicate that: a) a large number of related studies in the eld are prone to speci c pitfalls that lead to over-estimated results in favour of ML tools; b) the use of gaussian mixture models as global features can provide a higher accuracy compared to other local feature sets; c) ML never outperform simple statistically-based estimators on highly-seasonal parame- ters, and providing error bars is key to objectively evaluating the relative performance of the ML tools used; and d) ML tools can be e ective for parameters that are slow- changing such as groundwater.