1,183 research outputs found
Surrogate Optimization of Deep Neural Networks for Groundwater Predictions
Sustainable management of groundwater resources under changing climatic
conditions require an application of reliable and accurate predictions of
groundwater levels. Mechanistic multi-scale, multi-physics simulation models
are often too hard to use for this purpose, especially for groundwater managers
who do not have access to the complex compute resources and data. Therefore, we
analyzed the applicability and performance of four modern deep learning
computational models for predictions of groundwater levels. We compare three
methods for optimizing the models' hyperparameters, including two surrogate
model-based algorithms and a random sampling method. The models were tested
using predictions of the groundwater level in Butte County, California, USA,
taking into account the temporal variability of streamflow, precipitation, and
ambient temperature. Our numerical study shows that the optimization of the
hyperparameters can lead to reasonably accurate performance of all models (root
mean squared errors of groundwater predictions of 2 meters or less), but the
''simplest'' network, namely a multilayer perceptron (MLP) performs overall
better for learning and predicting groundwater data than the more advanced long
short-term memory or convolutional neural networks in terms of prediction
accuracy and time-to-solution, making the MLP a suitable candidate for
groundwater prediction.Comment: submitted to Journal of Global Optimization; main paper: 25 pages, 19
figures, 1 table; online supplement: 11 pages, 18 figures, 3 table
Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management
[Extract] With the advent of computers a wide range of mathematical and numerical models have been developed with the intent of predicting or approximating parts of hyrdrologic cycle. Prior to the advent of conceptual process based models, physical hydraulic models, which are reduced scale representations of large hydraulic systems, were used commonly in water resources engineering. Fast development in the computational systems and numerical solutions of complex differential equations enabled development of conceptual models to represent physical systems. Thus, in the last two decades large number of mathematical models was developed to represent different processes in hydrological cycle
Attention U-Net as a surrogate model for groundwater prediction
Numerical simulations of groundwater flow are used to analyze and predict the
response of an aquifer system to its change in state by approximating the
solution of the fundamental groundwater physical equations. The most used and
classical methodologies, such as Finite Difference (FD) and Finite Element (FE)
Methods, use iterative solvers which are associated with high computational
cost. This study proposes a physics-based convolutional encoder-decoder neural
network as a surrogate model to quickly calculate the response of the
groundwater system. Holding strong promise in cross-domain mappings,
encoder-decoder networks are applicable for learning complex input-output
mappings of physical systems. This manuscript presents an Attention U-Net model
that attempts to capture the fundamental input-output relations of the
groundwater system and generates solutions of hydraulic head in the whole
domain given a set of physical parameters and boundary conditions. The model
accurately predicts the steady state response of a highly heterogeneous
groundwater system given the locations and piezometric head of up to 3 wells as
input. The network learns to pay attention only in the relevant parts of the
domain and the generated hydraulic head field corresponds to the target samples
in great detail. Even relative to coarse finite difference approximations the
proposed model is shown to be significantly faster than a comparative
state-of-the-art numerical solver, thus providing a base for further
development of the presented networks as surrogate models for groundwater
prediction
Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps
The ability for groundwater heat pumps to meet space heating and cooling
demands without relying on fossil fuels, has prompted their mass roll out in
dense urban environments. In regions with high subsurface groundwater flow
rates, the thermal plume generated from a heat pump's injection well can
propagate downstream, affecting surrounding users and reducing their heat pump
efficiency. To reduce the probability of interference, regulators often rely on
simple analytical models or high fidelity groundwater simulations to determine
the impact that a heat pump has on the subsurface aquifer and surrounding heat
pumps. These are either too inaccurate or too computationally expensive for
everyday use. In this work, a surrogate model was developed to provide a quick,
high accuracy prediction tool of the thermal plume generated by a heat pump
within heterogeneous subsurface aquifers. Three variations of a convolutional
neural network were developed that accepts the known groundwater Darcy
velocities as discrete two-dimensional inputs and predicts the temperature
within the subsurface aquifer around the heat pump. A data set consisting of
800 numerical simulation samples, generated from random permeability fields and
pressure boundary conditions, was used to provide pseudo-randomized Darcy
velocity fields as input fields and the temperature field solution for training
the network. The subsurface temperature field output from the network provides
a more realistic temperature field that follows the Darcy velocity streamlines,
while being orders of magnitude faster than conventional high fidelity solversComment: 24 pages, 11 figure
Developing a cost-effective emulator for groundwater flow modeling using deep neural operators
Current groundwater models face a significant challenge in their
implementation due to heavy computational burdens. To overcome this, our work
proposes a cost-effective emulator that efficiently and accurately forecasts
the impact of abstraction in an aquifer. Our approach uses a deep neural
operator (DeepONet) to learn operators that map between infinite-dimensional
function spaces via deep neural networks. The goal is to infer the distribution
of hydraulic head in a confined aquifer in the presence of a pumping well. We
successfully tested the DeepONet on four problems, including two forward
problems, an inverse analysis, and a nonlinear system. Additionally, we propose
a novel extension of the DeepONet-based architecture to generate accurate
predictions for varied hydraulic conductivity fields and pumping well locations
that are unseen during training. Our emulator's predictions match the target
data with excellent performance, demonstrating that the proposed model can act
as an efficient and fast tool to support a range of tasks that require
repetitive forward numerical simulations or inverse simulations of groundwater
flow problems. Overall, our work provides a promising avenue for developing
cost-effective and accurate groundwater models
Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization
Approximation surrogates are used to substitute the numerical simulation model within optimization algorithms in order to reduce the computational burden on the coupled simulation-optimization methodology. Practical utility of the surrogate-based simulation-optimization have been limited mainly due to the uncertainty in surrogate model simulations. We develop a surrogate-based coupled simulation-optimization methodology for deriving optimal extraction strategies for coastal aquifer management considering the predictive uncertainty of the surrogate model. Optimization models considering two conflicting objectives are solved using a multiobjective genetic algorithm. Objectives of maximizing the pumping from production wells and minimizing the barrier well pumping for hydraulic control of saltwater intrusion are considered. Density-dependent flow and transport simulation model FEMWATER is used to generate input-output patterns of groundwater extraction rates and resulting salinity levels. The nonparametric bootstrap method is used to generate different realizations of this data set. These realizations are used to train different surrogate models using genetic programming for predicting the salinity intrusion in coastal aquifers. The predictive uncertainty of these surrogate models is quantified and ensemble of surrogate models is used in the multiple-realization optimization model to derive the optimal extraction strategies. The multiple realizations refer to the salinity predictions using different surrogate models in the ensemble. Optimal solutions are obtained for different reliability levels of the surrogate models. The solutions are compared against the solutions obtained using a chance-constrained optimization formulation and single-surrogate-based model. The ensemble-based approach is found to provide reliable solutions for coastal aquifer management while retaining the advantage of surrogate models in reducing computational burden
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