  Many water-quality monitoring programs aim to measure turbidity to help guide
effective management of waterways and catchments, yet distributing turbidity
sensors throughout networks is typically cost prohibitive. To this end, we
built and compared the ability of dynamic regression (ARIMA), long short-term
memory neural nets (LSTM), and generalized additive models (GAM) to forecast
stream turbidity one step ahead, using surrogate data from relatively low-cost
in-situ sensors and publicly available databases. We iteratively trialled
combinations of four surrogate covariates (rainfall, water level, air
temperature and total global solar exposure) selecting a final model for each
type that minimised the corrected Akaike Information Criterion.
Cross-validation using a rolling time-window indicated that ARIMA, which
included the rainfall and water-level covariates only, produced the most
accurate predictions, followed closely by GAM, which included all four
covariates. We constructed a meta-model, trained on time-series features of
turbidity, to take advantage of the strengths of each model over different time
points and predict the best model (that with the lowest forecast error one-step
prior) for each time step. The meta-model outperformed all other models,
indicating that this methodology can yield high accuracy and may be a viable
alternative to using measurements sourced directly from turbidity-sensors where
costs prohibit their deployment and maintenance, and when predicting turbidity
across the short term. Our findings also indicated that temperature and
light-associated variables, for example underwater illuminance, may hold
promise as cost-effective, high-frequency surrogates of turbidity, especially
when combined with other covariates, like rainfall, that are typically measured
at coarse levels of spatial resolution.
