Prediction of Water Hyacinth Coverage on Hartbeespoort Dam

Abstract

Water hyacinth is an invasive weed contributing to Hartbeespoort Dam’s poor water quality. Although biological control is the most effective and sustainable method of controlling water hyacinth, the dam has unfavourable conditions for agents that the weed thrives in. Literature uses mathematical models and remote sensing to theorise growth rates or estimate coverage. However, prediction could prove beneficial as planning biological control is essential to its success. Hence, a model to predict water hyacinth coverage was developed. This research simplified the complex relationships involved in water hyacinth growth to focus on the most influential factors: temperature and nutrients. Missing data were imputed using multiple k-nearest neighbours. Nutrient datasets had limited data, thus five scenarios were developed to extrapolate datasets, using Monte Carlo simulation and seasonal patterns. The features were used to build ensemble, decision tree, artificial neural network and support vector machine models. Ensemble using the bagging method was the best model resulting in a root mean square error of 4.01 for water hyacinth coverage predictions from 1 June 2018 to 1 May 2019.