Combining multivariate statistical techniques and random forests model to assess and diagnose the trophic status of Poyang Lake in China

TL;DR: The need for adaptive lake eutrophication management for floodplain lakes is emphasized, particularly the consideration of the strong trade-off and synergies between hydroclimatic conditions and water quality variations.

TL;DR: The ability of LSTM to simulate the forecast capacity of a complicated water quality model (i.e., environmental fluid dynamics code, EFDC) is investigated and the results reveal that the mechanism-guided L STM in this study may capture certain mechanism features.

TL;DR: In this article, the authors presented an approach for an increased understanding of NPS pollution characteristics and controls during events (intra-event) and between events (inter-event), by combining monitoring, modeling, and multivariate statistics.

TL;DR: Wang et al. as discussed by the authors used the fuzzy matter-element evaluation (FME) model to integrate key water quality indicators into a rank value to evaluate the water quality variations at spatial, seasonal and annual scales in Poyang Lake.

TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.

TL;DR: Human alteration of Earth is substantial and growing as discussed by the authors, between one-third and one-half of the land surface has been transformed by human action; the carbon dioxide concentration in the atmosphere has increased by nearly 30 percent since the beginning of the Industrial Revolution; more atmospheric nitrogen is fixed by humanity than by all natural terrestrial sources combined; more than half of all accessible surface fresh water is put to use by humanity; and about one-quarter of the bird species on Earth have been driven to extinction.

TL;DR: High classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods are observed.