Applying deep learning algorithms to enhance simulations of large-scale groundwater flow in IoTs

Yu Sen Su, Chuen Fa Ni, Wei Ci Li, I. Hsien Lee, Chi Ping Lin

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Deep learning for enhancing simulation IoTs groundwater flow is a good solution for gaining insights into the behavior of aquifer systems. In previous studies, corresponding results give a basis for the rational management of groundwater resources. The users generally require special skills or knowledge and massive observations in representing the field reality to perform the deep learning algorithms and simulations. To simplify the procedures for performing the numerical and large-scale groundwater flow simulations, we apply the deep learning algorithms which combine both the numerical groundwater model and large-scale IoTs, groundwater flow measuring equipment and various complex groundwater numerical models. The mechanism has the capability to show spatial distributions of in-situ data, analyze the spatial relationships of observed data, generate meshes, update users’ databases with in-situ observed data, and create professional reports. According to the numerical simulation results, we revealed that the deep learning algorithms are high computational efficiency, and we can enhance precise variance estimations for large-scale groundwater flow problems. The findings help users to best apply the deep learning algorithms in an easier way, get more accurate simulation results, and manage the groundwater resources rationally.

Original languageEnglish
Article number106298
JournalApplied Soft Computing Journal
Volume92
DOIs
StatePublished - Jul 2020

Keywords

  • Deep learning
  • Groundwater
  • Internet of Things
  • Numerical simulation

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