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A Deep Learning-based Novel Approach to Generate Continuous Daily Stream Nitrate Concentration for Nitrate Data-sparse Watersheds by Gourab Kumer Saha; Farshid Rahmani; Chaopeng Shen; Li Li; Raj Cibin is a Environmental Science article available to read on EtoBox.

What is A Deep Learning-based Novel Approach to Generate Continuous Daily Stream Nitrate Concentration for Nitrate Data-sparse Watersheds about?

High-frequency stream nitrate concentration provides critical insights into nutrient dynamics and can help to improve the effectiveness of management decisions to maintain a sustainable ecosystem. However, nitrate monitoring is conventionally conducted through lab analysis using in situ water samples and is typically at coarse temporal resolution. In the last decade, many agencies started collecting high-frequency (5–60 min intervals) nitrate data using optical sensors. The hypothesis of the study is that the data-driven models can learn the trend and temporal variability in nitrate concentration from high-frequency sensor-based nitrate data in the region and generate continuous nitrate data for unavailable data periods and data-limited locations. A Long Short-Term Memory (LSTM) model-based framework was developed to estimate continuous daily stream nitrate for dozens of gauge locations in Iowa, USA. The promising results supported the hypothesis; the LSTM model demonstrated median test-period Nash-Sutcliffe efficiency (NSE) = 0.75 and RMSE = 1.53 mg/L for estimating continuous daily nitrate concentration in 42 sites, which are unprecedented performance levels. Twenty-one sites (50

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Author

Gourab Kumer Saha; Farshid Rahmani; Chaopeng Shen; Li Li; Raj Cibin

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)