The Effect of Different Optimization Strategies to Physics-Constrained
  Deep Learning for Soil Moisture Estimation
Jianxin Xie Bing Yao Zheyu Jiang
Soil moisture is a key hydrological parameter that has significant importance
to human society and the environment. Accurate modeling and monitoring of soil
moisture in crop fields, especially in the root zone (top 100 cm of soil), is
essential for improving agricultural production and crop yield with the help of
precision irrigation and farming tools. Realizing the full sensor data
potential depends greatly on advanced analytical and predictive domain-aware
models. In this work, we propose a physics-constrained deep learning (P-DL)
framework to integrate physics-based principles on water transport and water
sensing signals for effective reconstruction of the soil moisture dynamics. We
adopt three different optimizers, namely Adam, RMSprop, and GD, to minimize the
loss function of P-DL during the training process. In the illustrative case
study, we demonstrate the empirical convergence of Adam optimizers outperforms
the other optimization methods in both mini-batch and full-batch training.