Ensemble machine learning for modeling greenhouse gas emissions at different time scales from irrigated paddy fields

Abbasi, 2021, Modelling carbon dioxide emissions under a maize-soy rotation using machine learning, Biosyst. Eng., 212, 1, 10.1016/j.biosystemseng.2021.09.013 Abdalla, 2020, Calibration and validation of the DNDC model to estimate nitrous oxide emissions and crop productivity for a summer maize-winter wheat double cropping system in Hebei, China, Environ. Pollut., 262, 10.1016/j.envpol.2020.114199 Àgueda, 2017, Temporal and spatial variability of ground level atmospheric methane concentrations in the Ebro River Delta, Atmos. Pollut. Res., 8, 741, 10.1016/j.apr.2017.01.009 Barton, 2015, Sampling frequency affects estimates of annual nitrous oxide fluxes, Sci. Rep., 5, 15912, 10.1038/srep15912 Barzegar, 2019, Using bootstrap ELM and LSSVM models to estimate river ice thickness in the Mackenzie River Basin in the Northwest Territories, Canada, J. Hydrol., 577, 10.1016/j.jhydrol.2019.06.075 Begum, 2019, Modelling greenhouse gas emissions and mitigation potentials in fertilized paddy rice fields in Bangladesh, Geoderma, 341, 206, 10.1016/j.geoderma.2019.01.047 Bennetzen, 2016, Decoupling of greenhouse gas emissions from global agricultural production: 1970-2050, Glob. Chang Biol., 22, 763, 10.1111/gcb.13120 Breiman, 2001, Random forests, Mach. Learn, 45, 5, 10.1023/A:1010933404324 Brilli, 2017, Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes, Sci. Total Environ., 598, 445, 10.1016/j.scitotenv.2017.03.208 Cai, 2017, Effects of biochar on CH4 emission with straw application on paddy soil, J. Soils Sediment., 18, 599, 10.1007/s11368-017-1761-x Cao, 2021, Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches, Agric. For. Meteorol., 297, 10.1016/j.agrformet.2020.108275 Chang, 2021, A machine-learning method of predicting vital capacity plateau value for ventilatory pump failure based on data mining, Healthc. (Basel, Switz. ), 9 Chang, 2020, An ensemble learning based hybrid model and framework for air pollution forecasting, Environ. Sci. Pollut. Res. Int., 27, 38155, 10.1007/s11356-020-09855-1 Dai, 2019, Variations and drivers of methane fluxes from a rice-wheat rotation agroecosystem in eastern China at seasonal and diurnal scales, Sci. Total Environ., 690, 973, 10.1016/j.scitotenv.2019.07.012 Dong, 2021, Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm, Neurocomputing, 462, 169, 10.1016/j.neucom.2021.07.084 Feng, 2021, Effects of irrigation regime and rice variety on greenhouse gas emissions and grain yields from paddy fields in central China, Agric. Water Manag., 250, 10.1016/j.agwat.2021.106830 Friedman, 2002, Stochastic gradient boosting, Comput. Stat. Data Anal., 38, 367, 10.1016/S0167-9473(01)00065-2 Fu, 2019, Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms, Front Plant Sci., 10, 730, 10.3389/fpls.2019.00730 Giltrap, 2010, DNDC: A process-based model of greenhouse gas fluxes from agricultural soils, Agric., Ecosyst. Environ., 136, 292, 10.1016/j.agee.2009.06.014 Glenn, 2021, Soil nitrous oxide emissions from no-till canola production under variable rate nitrogen fertilizer management, Geoderma, 385, 10.1016/j.geoderma.2020.114857 Goodrich, 2021, Improved gap filling approach and uncertainty estimation for eddy covariance N2O fluxes, Agric. For. Meteorol., 297, 10.1016/j.agrformet.2020.108280 Guo, 2021, Integrated phenology and climate in rice yields prediction using machine learning methods, Ecol. Indic., 120, 10.1016/j.ecolind.2020.106935 Hamrani, 2020, Machine learning for predicting greenhouse gas emissions from agricultural soils, Sci. Total Environ., 741, 10.1016/j.scitotenv.2020.140338 Hastie, 2009, The Elements of Statistical Learning, 1 He, 2020, Managing irrigation water for sustainable rice production in China, J. Clean. Prod., 245, 10.1016/j.jclepro.2019.118928 Hou, 2012, Seasonal variations of CH4 and N2O emissions in response to water management of paddy fields located in Southeast China, Chemosphere, 89, 884, 10.1016/j.chemosphere.2012.04.066 Irvin, J., Zhou, S., McNicol, G., Lu, F., Liu, V., Fluet-Chouinard, E., Ouyang, Z., Knox, S.H., Lucas-Moffat, A., Trotta, C., Papale, D., Vitale, D., Mammarella, I., Alekseychik, P., Aurela, M., Avati, A., Baldocchi, D., Bansal, S., Bohrer, G., Campbell, D.I., Chen, J., Chu, H., Dalmagro, H.J., Delwiche, K.B., Desai, A.R., Euskirchen, E., Feron, S., Goeckede, M., Heimann, M., Helbig, M., Helfter, C., Hemes, K.S., Hirano, T., Iwata, H., Jurasinski, G., Kalhori, A., Kondrich, A., Lai, D.Y.F., Lohila, A., Malhotra, A., Merbold, L., Mitra, B., Ng, A., Nilsson, M.B., Noormets, A., Peichl, M., Rey-Sanchez, A.C., Richardson, A.D., Runkle, B.R.K., Schäfer, K.V.R., Sonnentag, O., Stuart-Haëntjens, E., Sturtevant, C., Ueyama, M., Valach, A.C., Vargas, R., Vourlitis, G.L., Ward, E.J., Wong, G.X., Zona, D., Alberto, M.C.R., Billesbach, D.P., Celis, G., Dolman, H., Friborg, T., Fuchs, K., Gogo, S., Gondwe, M.J., Goodrich, J.P., Gottschalk, P., Hörtnagl, L., Jacotot, A., Koebsch, F., Kasak, K., Maier, R., Morin, T.H., Nemitz, E., Oechel, W.C., Oikawa, P.Y., Ono, K., Sachs, T., Sakabe, A., Schuur, E.A., Shortt, R., Sullivan, R.C., Szutu, D.J., Tuittila, E.-S., Varlagin, A., Verfaillie, J.G., Wille, C., Windham-Myers, L., Poulter, B., Jackson, R.B., 2021, Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands. Agricultural and Forest Meteorology 308–309, 108528. Jain, 2021, Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection, Sci. Comput. Program., 212, 10.1016/j.scico.2021.102713 Jiang, 2019, Mitigating greenhouse gas emissions in subsurface-drained field using RZWQM2, Sci. Total Environ., 646, 377, 10.1016/j.scitotenv.2018.07.285 Jiang, 2019, Water management to mitigate the global warming potential of rice systems: A global meta-analysis, Field Crops Res., 234, 47, 10.1016/j.fcr.2019.02.010 Jiang, 2021, Modeling climate change effects on rice yield and soil carbon under variable water and nutrient management, Sustainability, 13 Jiang, 2021, Biochar improved soil health and mitigated greenhouse gas emission from controlled irrigation paddy field: Insights into microbial diversity, J. Clean. Prod., 318, 10.1016/j.jclepro.2021.128595 Kamir, 2020, Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods, ISPRS J. Photogramm. Remote Sens., 160, 124, 10.1016/j.isprsjprs.2019.11.008 Karimi, 2019, Automated pipeline for photovoltaic module electroluminescence image processing and degradation feature classification, IEEE J. Photovolt., 9, 1324, 10.1109/JPHOTOV.2019.2920732 Kliegr, 2021, A review of possible effects of cognitive biases on interpretation of rule-based machine learning models, Artif. Intell., 295, 10.1016/j.artint.2021.103458 Lei, 2015, Nitrous oxide emission from the littoral zones of the Miyun Reservoir near Beijing, China, Hydrol. Res., 46, 811, 10.2166/nh.2014.095 Li, 2009, KNN-Based Model. Its Appl. Aftershock Predict., 83 Li, 2013, Emissions of CH4 and CO2 from double rice cropping systems under varying tillage and seeding methods, Atmos. Environ., 80, 438, 10.1016/j.atmosenv.2013.08.027 Li, 2021, Clay-hydrochar composites mitigated CH4 and N2O emissions from paddy soil: A whole rice growth period investigation, Sci. Total Environ., 780, 10.1016/j.scitotenv.2021.146532 Li, 2020, Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm, IEEE Access, 8, 68874, 10.1109/ACCESS.2020.2985381 Liu, 2013, Methane and nitrous oxide emissions from direct-seeded and seedling-transplanted rice paddies in southeast China, Plant Soil, 374, 285, 10.1007/s11104-013-1878-7 Lloyd, 2019, Greenhouse gas emissions from selected horticultural production systems in a cold temperate climate, Geoderma, 349, 45, 10.1016/j.geoderma.2019.04.030 Martre, 2015, Multimodel ensembles of wheat growth: many models are better than one, 21, 911 Modaresi, 2017, A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions, Water Resour. Manag., 32, 243, 10.1007/s11269-017-1807-2 Nguyen, 2022, Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms, J. Environ. Manag., 301, 10.1016/j.jenvman.2021.113868 Olu-Ajayi, 2022, Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques, J. Build. Eng., 45 Peng, 2020, Machine learning method for energy consumption prediction of ships in port considering green ports, J. Clean. Prod., 264, 10.1016/j.jclepro.2020.121564 Phyo, 2022, Short-term energy forecasting using machine-learning-based ensemble voting regression, Symmetry, 14, 160, 10.3390/sym14010160 Picoli, 2018, Big earth observation time series analysis for monitoring Brazilian agriculture, ISPRS J. Photogramm. Remote Sens., 145, 328, 10.1016/j.isprsjprs.2018.08.007 Pouladi, 2019, Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging, Geoderma, 342, 85, 10.1016/j.geoderma.2019.02.019 Qin, 2018, Abundance of transcripts of functional gene reflects the inverse relationship between CH4 and N2O emissions during mid-season drainage in acidic paddy soil, Biol. Fertil. Soils, 54, 885, 10.1007/s00374-018-1312-7 Romeiko, 2020, Spatially and temporally explicit life cycle environmental impacts of soybean production in the U.S. Midwest, Environ. Sci. Technol., 54, 4758, 10.1021/acs.est.9b06874 Saha, 2021, Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems, Environ. Res. Lett., 16, 10.1088/1748-9326/abd2f3 Shahhosseini, 2021, Corn yield prediction with ensemble CNN-DNN, Front Plant Sci., 12, 10.3389/fpls.2021.709008 Shahid, 2021, Intelligent forecast engine for short-term wind speed prediction based on stacked long short-term memory, Neural Comput. Appl., 33, 13767, 10.1007/s00521-021-06016-4 Shang, 2021, Can cropland management practices lower net greenhouse emissions without compromising yield?, Glob. Chang Biol., 27, 4657, 10.1111/gcb.15796 Shcherbak, 2014, Global metaanalysis of the nonlinear response of soil nitrous oxide (N2O) emissions to fertilizer nitrogen, Proc. Natl. Acad. Sci. USA, 111, 9199, 10.1073/pnas.1322434111 Simmonds, 2015, Modeling methane and nitrous oxide emissions from direct-seeded rice systems, J. Geophys. Res.: Biogeosci., 120, 2011, 10.1002/2015JG002915 Smith, 2008, Greenhouse gas mitigation in agriculture, Philos. Trans. R. Soc. Lond. B Biol. Sci., 363, 789, 10.1098/rstb.2007.2184 Smith, 2013, How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals?, Glob. Chang Biol., 19, 2285, 10.1111/gcb.12160 Sun, 2020, Re-estimating methane emissions from Chinese paddy fields based on a regional empirical model and high-spatial-resolution data, Environ. Pollut., 265, 10.1016/j.envpol.2020.115017 Taki, 2018, Comparison of two gap filling techniques for nitrous oxide fluxes from agricultural soil, Can. J. Soil Sci., 99 Tariq, 2017, Early drainage mitigates methane and nitrous oxide emissions from organically amended paddy soils, Geoderma, 304, 49, 10.1016/j.geoderma.2016.08.022 Torres-Barrán, 2019, Regression tree ensembles for wind energy and solar radiation prediction, Neurocomputing, 326–327, 151, 10.1016/j.neucom.2017.05.104 Tyralis, 2019, Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS, J. Hydrol., 577, 10.1016/j.jhydrol.2019.123957 Tyralis, 2020, Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms, Neural Comput. Appl., 33, 3053, 10.1007/s00521-020-05172-3 Wang, 2011, A comparative assessment of ensemble learning for credit scoring, Expert Syst. Appl., 38, 223, 10.1016/j.eswa.2010.06.048 Wang, 2020, Water-saving irrigation is a ‘win-win’ management strategy in rice paddies – With both reduced greenhouse gas emissions and enhanced water use efficiency, Agric. Water Manag., 228, 10.1016/j.agwat.2019.105889 Wang, 2019, Remarkable N2O emissions by draining fallow paddy soil and close link to the ammonium-oxidizing archaea communities, Sci. Rep., 9, 2550, 10.1038/s41598-019-39465-y Wang, 2017, Relationships between the potential production of the greenhouse gases CO2, CH4 and N2O and soil concentrations of C, N and P across 26 paddy fields in southeastern China, Atmos. Environ., 164, 458, 10.1016/j.atmosenv.2017.06.023 Wecking, 2020, Reconciling annual nitrous oxide emissions of an intensively grazed dairy pasture determined by eddy covariance and emission factors, Agric., Ecosyst. Environ., 287, 10.1016/j.agee.2019.106646 Wolpert, 1992, Stacked generalization, Neural Netw., 5, 241, 10.1016/S0893-6080(05)80023-1 Wu, 2020, On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression, IEEE Trans. Fuzzy Syst., 28, 2570, 10.1109/TFUZZ.2019.2941697 Xu, 2015, Controlling light quality and intensity can reduce N2O and CO2 emissions of mature aging rice, Greenh. Gases: Sci. Technol., 6, 308, 10.1002/ghg.1565 Yang, 2019, Biochar improved rice yield and mitigated CH4 and N2O emissions from paddy field under controlled irrigation in the Taihu Lake Region of China, Atmos. Environ., 200, 69, 10.1016/j.atmosenv.2018.12.003 Yu, 2015, Learning deep representations via extreme learning machines, Neurocomputing, 149, 308, 10.1016/j.neucom.2014.03.077 Zhang, 2019, Magnitude and Drivers of Potential Methane Oxidation and Production across the Tibetan Alpine Permafrost Region, Environ. Sci. Technol., 53, 14243, 10.1021/acs.est.9b03490 Zhang, 2017, Uncertainties in the national inventory of methane emissions from rice cultivation: field measurements and modeling approaches, Biogeosciences, 14, 163, 10.5194/bg-14-163-2017 Zhang, 2021, Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae, Bioresour. Technol., 342, 10.1016/j.biortech.2021.126011 Zhao, 2019, Management-induced greenhouse gases emission mitigation in global rice production, Sci. Total Environ., 649, 1299, 10.1016/j.scitotenv.2018.08.392