Estimating the Maximum Depth of Andean Lakes: A Comparative Analysis Using Machine Learning

Abstract

Multispectral modelling of 114 tropical Andean lakes in Southern Ecuador was implemented using observations of the maximum depth (Zmax). Five machine learning methods (MLMs), namely the multiple linear regression model (MLRM), generalised additive model (GAM), generalised linear model (GLM), multivariate adaptive regression splines (MARS), and random forest (RF), were applied on a LANDSAT 8 mosaic. Within the scope of a split-sample (SS) evaluation test, for each of the MLMs, a single model was developed for 70% (i.e., 80) of the studied lakes. Statistical measures and graphical inspection were used in the evaluation tests. An analysis of the absolute value of the model residuals (|res|) revealed that the MARS method outperformed the other MLMs. Nevertheless, a |res| > 10 m was observed for approximately 10% of the lakes. The worst predictions were produced by the GLM. These findings were confirmed in the model validation phase (SS test). With the exception of the GLM, the MLMs correctly predicted whether a lake was shallow or deep in more than 80% of the cases. In a more stringent multi-site (MS) test, the performance of the five Zmax models was assessed in predicting the bathymetry of 11,636 pixels that were not considered when fitting the models. Once more, MARS outperformed the other MLMs. However, a |res| > 10 m for 20% of the pixels was observed. Nevertheless, the quality of the predictions may still be regarded as acceptable for management purposes. Promising multispectral bathymetric predictions could be obtained, even with only a limited number of observations. The evaluation tests used in this pioneering study could be easily replicated elsewhere.