Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …

Irrigation accounts for~ 70% of global freshwater withdrawals and~ 90% of consumptive
water use, driving myriad Earth system impacts. In this Review, we summarize how irrigation …

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling.
The tremendous potential benefits of SA are, however, yet to be fully realized, both for …

Literature reviews play an essential role in academic research to gather existing knowledge
and to examine the state of a field. However, researchers in business, management and …

This study presents a bibliometric analysis of scientific publications investigating
entrepreneurial universities in the business and management fields. The authors collected …

This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …

Abstract Model calibration and validation are critical in hydrological model robustness
assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data …

El propósito de este artículo es realizar una revisión sistemática y un análisis bibliométrico
de la producción científica relacionada con la Responsabilidad Social Universitaria, a través …

Long short-term memory (LSTM) models are recurrent neural networks from the field of deep
learning (DL) which have shown promise for time series modelling, especially in conditions …

The use of structured literature review methods like bibliometric analysis is growing in the
management fields, but there is limited knowledge on how they can be facilitated by …