Long‐Term Forecast of Water Temperature and Dissolved Oxygen Profiles in Deep Lakes Using Artificial Neural Networks Conjugated With Wavelet Transform
Document Type
Article
Publication Date
12-11-2019
Publication Title
Limnology and Oceanography
Volume
65
Issue
6
First page number:
1297
Last page number:
1317
Abstract
Forecasting water quality in inland waters can improve management practices to protect water resources. This study proposes a novel data‐driven framework to forecast water quality profiles over long time periods in Boulder Basin of Lake Mead, a deep monomictic subtropical lake. Hourly meteorological data were used to estimate lake–atmosphere heat exchange. Heat fluxes combined with 6‐hourly measured water quality profiles up to 106 m depth were used to train six different artificial neural networks to forecast water temperature, dissolved oxygen, and conductivity profiles up to 240 d ahead. A model incorporating heat fluxes, winds, and stationary wavelet decomposition generated correlation coefficients... (See full abstract in article).
Disciplines
Climate | Oceanography
Language
English
Repository Citation
Saber, A.,
James, D. E.,
Hayes, D. F.
(2019).
Long‐Term Forecast of Water Temperature and Dissolved Oxygen Profiles in Deep Lakes Using Artificial Neural Networks Conjugated With Wavelet Transform.
Limnology and Oceanography, 65(6),
1297-1317.
http://dx.doi.org/10.1002/lno.11390
