arXiv subjects

Mengyi Gong

Publications and source records attributed to Mengyi Gong.

3 recordsLinked to original sources

arXiv2026

An ensemble prediction method for forecasting sap flux density and water-use in temperate trees

Efficient irrigation management is crucial to agriculture, forestry and horticulture, especially under climate change. Developments in novel sensors and Internet of Things technology provide an opportunity to carry out real-time monitoring of tree sap flux density, which, when coupled with advanced modelling techniques, enables online prediction of tree water-use suitable for irrigation planning. This manuscript proposes one such pipeline that integrates tree sap flow sensors, weather station sensors, and statistical models to predict tree daily water-use. In particular, an ensemble prediction approach based on additive models has been developed, using weather data as the main predictors of sap flux density. The method simultaneously considers the non-linear relationships and interactions between sap flux density and its environmental drivers, as well as the variability among individual trees over different growing seasons. Using field data collected on nine species of trees over the 2022, 2023 and 2024 growing seasons, this manuscript demonstrates the ability of the proposed ensemble prediction method in producing reliable daily water-use forecasts. The challenge of predicting tree water-use under climate stress, such as heatwaves, and the impact of tree sizes on prediction have also been discussed. Despite the complexity of the problem, the proposed method provides a general framework which can be used in a variety of settings, from commercial tree growers to conversation work. The model can be integrated into an online monitoring platform, assisting real-time decision making on irrigation management.

stat.AP↗

arXiv2025

Inferring Soil Drydown Behaviour with Adaptive Bayesian Online Changepoint Analysis

Continuous soil-moisture measurements provide a direct lens on subsurface hydrological processes, notably the post-rainfall "drydown" phase. Because these records consist of distinct, segment-specific behaviours whose forms and scales vary over time, realistic inference demands a model that captures piecewise dynamics while accommodating parameters that are unknown a priori. Building on Bayesian Online Changepoint Detection (BOCPD), we introduce two complementary extensions: a particle-filter variant that substitutes exact marginalisation with sequential Monte Carlo to enable real-time inference when critical parameters cannot be integrated out analytically, and an online-gradient variant that embeds stochastic gradient updates within BOCPD to learn application-relevant parameters on the fly without prohibitive computational cost. After validating both algorithms on synthetic data that replicate the temporal structure of field observations-detailing hyperparameter choices, priors, and cost-saving strategies-we apply them to soil-moisture series from experimental sites in Austria and the United States, quantifying site-specific drydown rates and demonstrating the advantages of our adaptive framework over static models.

stat.AP↗

arXiv2024

A changepoint approach to modelling non-stationary soil moisture dynamics

Soil moisture dynamics provide an indicator of soil health that scientists model via drydown curves. The typical modelling process requires the soil moisture time series to be manually separated into drydown segments and then exponential decay models are fitted to them independently. Sensor development over recent years means that experiments that were previously conducted over a few field campaigns can now be scaled to months or years at a higher sampling rate. To better meet the challenge of increasing data size, this paper proposes a novel changepoint-based approach to automatically identify structural changes in the soil drying process and simultaneously estimate the drydown parameters that are of interest to soil scientists. A simulation study is carried out to demonstrate the performance of the method in detecting changes and retrieving model parameters. Practical aspects of the method such as adding covariates and penalty learning are discussed. The method is applied to hourly soil moisture time series from the NEON data portal to investigate the temporal dynamics of soil moisture drydown. We recover known relationships previously identified manually, alongside delivering new insights into the temporal variability across soil types and locations.

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