Emulators of a Physical Model for Estimating Leaf Wetness Duration

Abstract

Leaf wetness duration (LWD) has rarely been measured due to lack of standard protocol. Thus, empirical and physical models have been proposed to resolve this gap. Although the physical model provides robust performance in diverse conditions, it requires many variables. The empirical model requires fewer variables; nevertheless, its performance is specific to a given condition. A universal LWD estimation model using fewer variables is thus needed to improve LWD estimation. The objective of this study was to develop emulators of the LWD estimation physical model for use as universal empirical models. It is assumed that the Penman–Monteith (PM) model determines LWD and can be employed as a physical model. In this study, a simulation was designed and conducted to investigate the characteristics of the PM model and to build the emulators. The performances of the built emulators were evaluated based on a case study of LWD data obtained in South Korea. It was determined that a machine learning algorithm can properly emulate the PM model in LWD estimations based on the simulation. Moreover, the poor performances of some emulators that use wind speed may have been due to the limitation of wind speed measurement. The accuracy of the anemometer is thus critical to estimating LWD using physical models. A deep neural network using relative humidity and air temperature was found to be the most appropriate emulator of those tested for LWD estimation.

Keywords

• leaf wetness duration
• Penman–Monteith
• deep neural network
• machine learning