2024
1.
Ozmen Erkin Kokten; Raviv Raich; James Holmes; Alan Fern
Learning Extended Forecasts of Soil Water Content via Physically-Inspired Autoregressive Models Proceedings Article
In: 2024 International Conference on Machine Learning and Applications (ICMLA), pp. 400–407, 2024, (ISSN: 1946-0759).
Abstract | Links | BibTeX | Tags: autoregressive training, non-linear state-space models, Pipelines, Predictive models, Soil measurements, Soil Water Content, State-space methods, Stress, teacher-forcing, time-series, Training, Training data, Weather forecasting
@inproceedings{kokten_learning_2024,
title = {Learning Extended Forecasts of Soil Water Content via Physically-Inspired Autoregressive Models},
author = {Ozmen Erkin Kokten and Raviv Raich and James Holmes and Alan Fern},
url = {https://ieeexplore.ieee.org/abstract/document/10903312},
doi = {10.1109/ICMLA61862.2024.00060},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
booktitle = {2024 International Conference on Machine Learning and Applications (ICMLA)},
pages = {400\textendash407},
abstract = {Vine stress resulting from soil water content (SWC) restrictions allows growers to improve grape and subsequent wine quality. In this work, we consider learning models that can forecast SWC to assist growers' irrigation decisions. In particular, we investigate training auto-regressive recurrent neural networks to make multi-day hourly forecasts of SWC based on historical data from soil-moisture sensors, irrigation sched-ules, and evapotranspiration estimates. Our work addresses two practical challenges in training such models. First, trained auto-regressive models are prone to error propagation, which quickly degrades longer-term forecasts. Second, it is difficult to learn the underlying causal relationship between irrigation and soil moisture due to the training data having limited coverage of the primary control input, irrigation. We propose a training strategy that combines one-step teacher forcing loss with a loss over multi-step autoregressive predictions and novel regularization terms to ensure SWC forecasts align with scientific models, effectively addressing the key challenges. We present results from five irrigation blocks with two cultivars, using datasets ranging from 2947 to 4784 hourly measurements of SWC, irrigation, and weather. Our methodology achieves precise SWC predictions and generates realistic forecasts for untrained irrigation scenarios.},
note = {ISSN: 1946-0759},
keywords = {autoregressive training, non-linear state-space models, Pipelines, Predictive models, Soil measurements, Soil Water Content, State-space methods, Stress, teacher-forcing, time-series, Training, Training data, Weather forecasting},
pubstate = {published},
tppubtype = {inproceedings}
}
Vine stress resulting from soil water content (SWC) restrictions allows growers to improve grape and subsequent wine quality. In this work, we consider learning models that can forecast SWC to assist growers' irrigation decisions. In particular, we investigate training auto-regressive recurrent neural networks to make multi-day hourly forecasts of SWC based on historical data from soil-moisture sensors, irrigation sched-ules, and evapotranspiration estimates. Our work addresses two practical challenges in training such models. First, trained auto-regressive models are prone to error propagation, which quickly degrades longer-term forecasts. Second, it is difficult to learn the underlying causal relationship between irrigation and soil moisture due to the training data having limited coverage of the primary control input, irrigation. We propose a training strategy that combines one-step teacher forcing loss with a loss over multi-step autoregressive predictions and novel regularization terms to ensure SWC forecasts align with scientific models, effectively addressing the key challenges. We present results from five irrigation blocks with two cultivars, using datasets ranging from 2947 to 4784 hourly measurements of SWC, irrigation, and weather. Our methodology achieves precise SWC predictions and generates realistic forecasts for untrained irrigation scenarios.
