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Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivialfraction of global greenhouse gas (GHG) budget. To date, estimatingN2O fluxes from cropland remains a challenging task because the relatedmicrobial processes (e.g., nitrification and denitrification) are controlledby complex interactions among climate, soil, plant and human activities.Existing approaches such as process-based (PB) models have well-knownlimitations due to insufficient representations of the processes oruncertainties of model parameters, and due to leverage recent advances inmachine learning (ML) a new method is needed to unlock the “black box” toovercome its limitations such as low interpretability, out-of-sample failureand massive data demand. In this study, we developed a first-of-its-kindknowledge-guided machine learning model for agroecosystems (KGML-ag) byincorporating biogeophysical and chemical domain knowledge from an advanced PBmodel, ecosys, and tested it by comparing simulating daily N2O fluxes withreal observed data from mesocosm experiments. The gated recurrent unit (GRU)was used as the basis to build the model structure. To optimize the modelperformance, we have investigated a range of ideas, including (1) usinginitial values of intermediate variables (IMVs) instead of time series asmodel input to reduce data demand; (2) building hierarchical structures toexplicitly estimate IMVs for further N2O prediction; (3) using multi-tasklearning to balance the simultaneous training on multiple variables; and (4)pre-training with millions of synthetic data generated from ecosys and fine-tuningwith mesocosm observations. Six other pure ML models were developed usingthe same mesocosm data to serve as the benchmark for the KGML-ag model.Results show that KGML-ag did an excellent job in reproducing the mesocosmN2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1from cross validation). Importantly, KGML-ag always outperformsthe PB model and ML models in predicting N2O fluxes, especially forcomplex temporal dynamics and emission peaks. Besides, KGML-ag goes beyondthe pure ML models by providing more interpretable predictions as well aspinpointing desired new knowledge and data to further empower the currentKGML-ag. We believe the KGML-ag development in this study will stimulate anew body of research on interpretable ML for biogeochemistry and otherrelated geoscience processes. 

Point set is a major type of 3D structure representation format characterized by its data availability and compactness. Most former deep learning-based point set models pay equal attention to different point set regions and channels, thus having limited ability in focusing on small regions and specific channels that are important for characterizing the object of interest. In this paper, we introduce a novel model named Attention-based Point Network (AttPNet). It uses attention mechanism for both global feature masking and channel weighting to focus on characteristic regions and channels. There are two branches in our model. The first branch calculates an attention mask for every point. The second branch uses convolution layers to abstract global features from point sets, where channel attention block is adapted to focus on important channels. Evaluations on the ModelNet40 benchmark dataset show that our model outperforms the existing best model in classification tasks by 0.7% without voting. In addition, experiments on augmented data demonstrate that our model is robust to rotational perturbations and missing points. We also design a Electron Cryo-Tomography (ECT) point cloud dataset and further demonstrate our model’s ability in dealing with fine-grained structures on the ECT dataset.