Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources

arXiv:2308.09766v2 Announce Type: replace
Abstract: Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread predictions of hydrological variables such as river flow and water quality has become increasingly urgent due to climate and land use change over the past decades, and their associated impacts on water resources. Modern machine learning …

abstract arxiv challenge cs.lg dynamic environmental machine machine learning machine learning techniques management monitoring prediction predictions resources science series survey time series type variables water world