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HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network

Journal Article · · Ecological Informatics
 [1];  [2];  [3];  [4];  [5];  [6];  [1]
  1. Helmholtz Centre for Environmental Research (Germany)
  2. Univ. of Michigan, Ann Arbor, MI (United States)
  3. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  4. Vietnamese-German University (Vietnam)
  5. Thuyloi University (Vietnam)
  6. National Institute of Water and Atmospheric Research (NIWA) (New Zealand)
+ Show Author Affiliations
Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduced a GUI package, named HydroEcoLSTM, with the long short-term memory network (LSTM) as the core model, that allows non-ML experts to utilize their domain knowledge to construct complex ML models. We demonstrated the functionalities of HydroEcoLSTM with two practical examples, including (1) predictions of streamflow in both gauged and ungauged catchments and (2) predictions of multiple outputs (i.e., streamflow and isotope transport from two catchments). The simulation results obtained in both case experiments are satisfactory. In the first example, the average Nash–Sutcliffe Efficiency (NSE) for streamflow simulation during the testing period is 0.79 while the application of the trained model in two assumed ungauged catchments also achieves the average NSE of 0.68. In the second example, the average NSE for streamflow and instream isotope simulation during the testing period is 0.71. Ultimately, applications of HydroEcoLSTM with real-world examples demonstrate its potential use for practical applications and research without requiring extensive coding skills.
Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
2510856
Report Number(s):
PNNL-SA--207331
Journal Information:
Ecological Informatics, Journal Name: Ecological Informatics Vol. 85; ISSN 1574-9541
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
Ecological modeling
Hydrological modeling
Long short-term memory
Machine learning