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Impact of Input Feature Selection on Groundwater Level Prediction From a Multi-Layer Perceptron Neural Network

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  • معلومة اضافية
    • Publisher Information:
      eScholarship, University of California 2020-11-19
    • نبذة مختصرة :
      With the growing use of machine learning (ML) techniques in hydrological applications, there is a need to analyze the robustness, performance, and reliability of predictions made with these ML models. In this paper we analyze the accuracy and variability of groundwater level predictions obtained from a Multilayer Perceptron (MLP) model with optimized hyperparameters for different amounts and types of available training data. The MLP model is trained on point observations of features like groundwater levels, temperature, precipitation, and river flow in various combinations, for different periods and temporal resolutions. We analyze the sensitivity of the MLP predictions at three different test locations in California, United States and derive recommendations for training features to obtain accurate predictions. We show that the use of all available features and data for training the MLP does not necessarily ensure the best predictive performance at all locations. More specifically, river flow and precipitation data are important training features for some, but not all locations. However, we find that predictions made with MLPs that are trained solely on temperature and historical groundwater level measurements as features, without additional hydrological information, are unreliable at all locations.
    • الموضوع:
    • Availability:
      Open access content. Open access content
      CC-BY
    • Other Numbers:
      CDLER oai:escholarship.org:ark:/13030/qt6fm8t44n
      qt6fm8t44n
      https://escholarship.org/uc/item/6fm8t44n
      https://escholarship.org/
      1391573665
    • Contributing Source:
      UC MASS DIGITIZATION
      From OAIster®, provided by the OCLC Cooperative.
    • الرقم المعرف:
      edsoai.on1391573665
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