Title

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

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