Sensitivity analysis (SA) aims to identify the key parameters that affect model performance
and it plays important roles in model parameterization, calibration, optimization, and …

Crop models are essential tools for analysing the effects of climate variability, change on
crop growth and development and the potential impact of adaptation strategies. Despite their …

Nature-inspired metaheuristic approaches draw their core idea from biological evolution in
order to create new and powerful competing algorithms. Such algorithms can be divided into …

Detecting transformer faults is critical to avoid the undesirable loss of transformers from
service and ensure utility service continuity. Transformer faults diagnosis can be determined …

Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

Agent-based models are increasingly used to address questions regarding real-world
phenomena and mechanisms; therefore, the calibration of model parameters to certain data …

Several optimization problems from various types of applications have been efficiently
resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and …

The choice of sensitivity analysis methods for a model often relies on the behavior of model
outputs. However, many building energy models are “black-box” functions whose behavior …

This study includes a global sensitivity analysis of the water productivity model AquaCrop.
The study rationale consisted in a comprehensive evaluation of the model and the …

One of the relevant factors in smart energy management is the ability to predict the
consumption of energy in smart households and use the resulting data for planning and …