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Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks

Author

Listed:
  • Mohammad Taghi Sattari

    (University of Tabriz
    Ankara University)

  • Anca Avram

    (Technical University of Cluj-Napoca, North University Center of Baia Mare)

  • Halit Apaydin

    (Ankara University)

  • Oliviu Matei

    (Technical University of Cluj-Napoca, North University Center of Baia Mare)

Abstract

Precipitation is the most important element of the water cycle and an indispensable element of water resources management. This paper’s aim is to model the monthly precipitation in 8 precipitation observation stations in the province of Hamadan, Iran. The effects and role of different feature weights pre-processing methods (Weight by deviation, Weight by PCA, Weight by correlation and Weight by Support Vector Machine) on artificial intelligence modeling were investigated. Deep learning method based on a multi-layer feed-forward artificial neural network that is trained with Stochastic Gradient Descent using back-propagation (DL-SGD) and Convolutional Neural Networks (CNN) modelling were applied. The precipitation of each station is modeled using the precipitation values of the other stations. The best result, among all scenarios, at the Vasaj station according to the DL-SGD method (CC = 0.9845, NS = 0.9543 and RMSE = 10.4169 mm) and at the Varayineh station according to the CNN method (CC = 0.9679, NS = 0.9362 and RMSE = 16.0988 mm) were estimated.

Suggested Citation

  • Mohammad Taghi Sattari & Anca Avram & Halit Apaydin & Oliviu Matei, 2023. "Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 5871-5891, December.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:15:d:10.1007_s11269-023-03563-4
    DOI: 10.1007/s11269-023-03563-4
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