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Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

 

Training machine learning (ML) models for scientific problems is often challenging due to limited observation data. To overcome this challenge, prior works commonly pre-train ML models using simulated data before having them fine-tuned with small real data. Despite the promise shown in initial research across different domains, these methods cannot ensure improved performance after fine-tuning because (i) they are not designed for extracting generalizable physics-aware features during pre-training, (ii) the features learned from pre-training can be distorted by the fine-tuning process. In this paper, we propose a new learning method for extracting, preserving, and adapting physics-aware features. We build a knowledge-guided neural network (KGNN) model based on known dependencies amongst physical variables, which facilitate extracting physics-aware feature representation from simulated data. Then we fine-tune this model by alternately updating the encoder and decoder of the KGNN model to enhance the prediction while preserving the physics-aware features learned through pre-training. We further propose to adapt the model to new testing scenarios via a teacher-student learning framework based on the model uncertainty. The results demonstrate that the proposed method outperforms many baselines by a good margin, even using sparse training data or under out-of-sample testing scenarios. 

Training machine learning (ML) models for scientific problems is often challenging due to limited observation data. To overcome this challenge, prior works commonly pre-train ML models using simulated data before having them fine-tuned with small real data. Despite the promise shown in initial research across different domains, these methods cannot ensure improved performance after fine-tuning because (i) they are not designed for extracting generalizable physics-aware features during pre-training, (ii) the features learned from pre-training can be distorted by the fine-tuning process. In this paper, we propose a new learning method for extracting, preserving, and adapting physics-aware features. We build a knowledge-guided neural network (KGNN) model based on known dependencies amongst physical variables, which facilitate extracting physics-aware feature representation from simulated data. Then we fine-tune this model by alternately updating the encoder and decoder of the KGNN model to enhance the prediction while preserving the physics-aware features learned through pre-training. We further propose to adapt the model to new testing scenarios via a teacher-student learning framework based on the model uncertainty. The results demonstrate that the proposed method outperforms many baselines by a good margin, even using sparse training data or under out-of-sample testing scenarios. 

Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data often exhibit complex patterns and significant data variability across different locations. The labels in many real-world applications can also be limited, which makes it difficult to separately train independent models for different locations. Although meta learning has shown promise in model adaptation with small samples, existing meta learning methods remain limited in handling a large number of heterogeneous tasks, e.g., a large number of locations with varying data patterns. To bridge the gap, we propose task-adaptive formulations and a model-agnostic meta-learning framework that transforms regionally heterogeneous data into location-sensitive meta tasks. We conduct task adaptation following an easy-to-hard task hierarchy in which different meta models are adapted to tasks of different difficulty levels. One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks. It also enhances the model generalization by automatically adapting the meta model of the corresponding difficulty level to any new tasks. We demonstrate the superiority of our proposed framework over a diverse set of baselines and state-of-the-art meta-learning frameworks. Our extensive experiments on real crop yield data show the effectiveness of the proposed method in handling spatial-related heterogeneous tasks in real societal applications.

 

This paper proposes a physics-guided neural network model to predict crop yield and maintain the fairness over space. Failures to preserve the spatial fairness in predicted maps of crop yields can result in biased policies and intervention strategies in the distribution of assistance or subsidies in supporting individuals at risk. Existing methods for fairness enforcement are not designed for capturing the complex physical processes that underlie the crop growing process, and thus are unable to produce good predictions over large regions under different weather conditions and soil properties. More importantly, the fairness is often degraded when existing methods are applied to different years due to the change of weather conditions and farming practices. To address these issues, we propose a physics-guided neural network model, which leverages the physical knowledge from existing physics-based models to guide the extraction of representative physical information and discover the temporal data shift across years. In particular, we use a reweighting strategy to discover the relationship between training years and testing years using the physics-aware representation. Then the physics-guided neural network will be refined via a bi-level optimization process based on the reweighted fairness objective. The proposed method has been evaluated using real county-level crop yield data and simulated data produced by a physics-based model. The results demonstrate that this method can significantly improve the predictive performance and preserve the spatial fairness when generalized to different years.

 

In many environmental applications, recurrent neural networks (RNNs) are often used to model physical variables with long temporal dependencies. However, due to minibatch training, temporal relationships between training segments within the batch (intra-batch) as well as between batches (inter-batch) are not considered, which can lead to limited performance. Stateful RNNs aim to address this issue by passing hidden states between batches. Since Stateful RNNs ignore intra-batch temporal dependency, there exists a trade-off between training stability and capturing temporal dependency. In this paper, we provide a quantitative comparison of different Stateful RNN modeling strategies, and propose two strategies to enforce both intra- and inter-batch temporal dependency. First, we extend Stateful RNNs by defining a batch as a temporally ordered set of training segments, which enables intra-batch sharing of temporal information. While this approach significantly improves the performance, it leads to much larger training times due to highly sequential training. To address this issue, we further propose a new strategy which augments a training segment with an initial value of the target variable from the timestep right before the starting of the training segment. In other words, we provide an initial value of the target variable as additional input so that the network can focus on learning changes relative to that initial value. By using this strategy, samples can be passed in any order (mini-batch training) which significantly reduces the training time while maintaining the performance. In demonstrating the utility of our approach in hydrological modeling, we observe that the most significant gains in predictive accuracy occur when these methods are applied to state variables whose values change more slowly, such as soil water and snowpack, rather than continuously moving flux variables such as streamflow. 

Process‐based land surface models are important tools for estimating global wetland methane (CH₄) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site‐level patterns of freshwater wetland CH₄fluxes (FCH₄) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model‐observation disagreements are mainly at multi‐day time scales (<15 days); (b) most of the models can capture the CH₄variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH₄production). Our evaluation suggests the need to accurately replicate FCH₄variability, especially at short time scales, in future wetland CH₄model developments.