Datasets to simulate data-driven models for filling the temporal gaps in the low-frequency nitrate data

This data was used to develop a DL model (LSTM model) based data-driven modeling framework for filling the temporal gaps in the low-frequency stream nitrate data. The dataset was prepared to test the effectiveness of the developed framework to estimate daily stream nitrate at low-frequency nitrate monitoring regions. Three river basins in Iowa, USA, including Des Moines River Basin, Iowa River Basin, and Cedar River Basin, were selected to test the developed framework's performance. This dataset contains forcing, attributes, and time frame for simulation data.

Citation

Saha, Gourab (2023). Datasets to simulate data-driven models for filling the temporal gaps in the low-frequency nitrate data [Data set]. Scholarsphere. https://doi.org/10.26207/28et-1e58

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Work Title Datasets to simulate data-driven models for filling the temporal gaps in the low-frequency nitrate data
Subtitle Input environmental data to estimate daily stream nitrate concentration
Access
Open Access
Creators
  1. Gourab Saha
Keyword
  1. Nitrate modeling
  2. LSTM
  3. Machine learning
  4. Deep learning
License CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike)
Work Type Dataset
Acknowledgments
  1. Jerry Mount and Christopher Jones from IWQIS and Felipe Quintero Duque and Kate Giannini from IIHR helped to get nitrate concentration and streamflow data information used in the study. The project was funded by the Penn State Institute of Energy and the Environment. R. Cibin is supported, in part, by the USDA National Institute of Food and Agriculture Federal Appropriations under Project PEN04574 and Accession number 1004448.
Publication Date October 15, 2023
Subject
  1. Deep learning application in hydrology and water quality
Language
  1. English
DOI doi:10.26207/28et-1e58
Geographic Area
  1. Iowa, USA
Deposited October 15, 2023

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Version 1
published

  • Created
  • Updated
  • Added Creator Gourab Saha
  • Added Read_Me.docx
  • Added ALL_Data.zip
  • Updated License Show Changes
    License
    • https://creativecommons.org/licenses/by-nc-sa/4.0/
  • Published
  • Updated

Version 2
published

  • Created
  • Updated Acknowledgments Show Changes
    Acknowledgments
    • Jerry Mount and Christopher Jones from IWQIS and Felipe Quintero Duque and Kate Giannini from IIHR helped to get nitrate concentration and streamflow data information used in the study. The project was funded by the Penn State Institute of Energy and the Environment. R. Cibin is supported, in part, by the USDA National Institute of Food and Agriculture Federal Appropriations under Project PEN04574 and Accession number 1004448.
  • Deleted Read_Me.docx
  • Added README.txt
  • Published
  • Updated Source, Keyword Show Changes
    Source
    • Multiple
    Keyword
    • nitrate modeling, LSTM, machine learning, deep learning
    • Nitrate modeling, LSTM, Machine learning, Deep learning
  • Updated