In simulation-based realization of complex systems, we are forced to address the issue of
computational complexity. One critical issue that must be addressed is the approximation of …

Process-based hydrological models have a long history dating back to the 1960s. Criticized
by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

This review presents machine learning (ML) approaches from an applied economist's
perspective. We first introduce the key ML methods drawing connections to econometric …

Reconstruction of natural streamflow is fundamental to the sustainable management of
water resources. In China, previous reconstructions from sparse and poor-quality gauge …

Abstract Model parameter calibration is a fundamentally important stage that must be
completed before applying a model to address practical problems. In this study, we describe …

Irrigation is critical to sustain agricultural productivity in dry or semi-dry environments, and
center pivots, due to their versatility and ruggedness, are the most widely used irrigation …

An efficient, black-box multiobjective optimization technique is presented, which is capable
of simultaneously optimizing designs for performance as well as robustness when input …

Surrogate models have been widely used in engineering design because of their capability
to approximate computationally complex engineering systems. In practice, the choice of …