Abstract
Precise estimation of physical hydrology components including groundwater levels (GWLs) is a challenging task, especially in relatively non-contiguous watersheds. This study estimates GWLs with deep learning and artificial neural networks (ANNs), namely a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network (CNN) with four different input variable combinations for two watersheds (Baltic River and Long Creek) in Prince Edward Island, Canada. Variables including stream level, stream flow, precipitation, relative humidity, mean temperature, evapotranspiration, heat degree days, dew point temperature, and evapotranspiration for the 2011–2017 period were used as input variables. Using a hit and trial approach and various hyperparameters, all ANNs were trained from scratched (2011–2015) and validated (2016–2017). The stream level was the major contributor to GWL fluctuation for the Baltic River and Long Creek watersheds (R2 = 50.8 and 49.1%, respectively). The MLP performed better in validation for Baltic River and Long Creek watersheds (RMSE = 0.471 and 1.15, respectively). Increased number of variables from 1 to 4 improved the RMSE for the Baltic River watershed by 11% and for the Long Creek watershed by 1.6%. The deep learning techniques introduced in this study to estimate GWL fluctuations are convenient and accurate as compared to collection of periodic dips based on the groundwater monitoring wells for groundwater inventory control and management.
Highlights
Groundwater is the major source of industrial and potable water supplies in Prince Edward Island, Canada [1]
The deep learning techniques introduced in this study to estimate groundwater levels (GWLs) fluctuations are convenient and accurate as compared to collection of periodic dips based on the groundwater monitoring wells for groundwater inventory control and management
The results suggested that the recurrent neural network (RNN) is the most efficient model compared to static structure artificial neural networks (ANNs)
Summary
Groundwater is the major source of industrial and potable water supplies in Prince Edward Island, Canada [1]. Over the past few years, there has been increased demand in the agriculture sector for supplemental irrigation, which poses several challenges for water and resource managers. An inventory of groundwater is necessary for efficient water resource management, especially in relation to growing groundwater demands for agricultural use. It is neither feasible nor economical to install and manage monitoring groundwater wells in a place like Prince Edward Island, which consists of 260 watersheds for efficient water management. The inventory control of the groundwater resource can ensure the sustainability of water resources in the areas where groundwater pumping is common for supplemental irrigation or for domestic use
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