Artificial neural networks for monitoring network optimisation—a practical example using a national insect survey
暂无分享,去创建一个
Alice E. Milne | Yoann Bourhis | James R. Bell | Frank van den Bosch | J. Bell | F. C. van den Bosch | A. Milne | Yoann Bourhis
[1] Paul H. Holloway,et al. Dynamic selection of environmental variables to improve the prediction of aphid phenology: A machine learning approach , 2018 .
[2] Paul Verrier,et al. Long-term phenological trends, species accumulation rates, aphid traits and climate: five decades of change in migrating aphids , 2014, The Journal of animal ecology.
[3] Francisco C. Pereira,et al. Beyond Expectation: Deep Joint Mean and Quantile Regression for Spatiotemporal Problems. , 2018, IEEE transactions on neural networks and learning systems.
[4] Peter A Henrys,et al. Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century , 2019, Global change biology.
[5] Tim G. Benton,et al. Linking agricultural practice to insect and bird populations: a historical study over three decades , 2002 .
[6] T. Legg,et al. HadUK‐Grid—A new UK dataset of gridded climate observations , 2019, Geoscience Data Journal.
[7] R. Webster,et al. Basic Steps in Geostatistics: The Variogram and Kriging , 2015, SpringerBriefs in Agriculture.
[8] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[9] Wenyan Wu,et al. On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data‐Driven Models , 2018 .
[10] Umberto Michelucci,et al. Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks , 2018 .
[11] Willem Waegeman,et al. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods , 2019, Machine Learning.
[12] Wenyan Wu,et al. A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks , 2013 .
[13] Mark Rounsevell,et al. Spatial autocorrelation as a tool for identifying the geographical patterns of aphid annual abundance , 2005 .
[14] Ian Osband,et al. Risk versus Uncertainty in Deep Learning: Bayes, Bootstrap and the Dangers of Dropout , 2016 .
[15] Lawrence W. Sheppard,et al. Changes in large-scale climate alter spatial synchrony of aphid pests , 2016 .
[16] Nicolai Meinshausen,et al. Quantile Regression Forests , 2006, J. Mach. Learn. Res..
[17] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[18] William A. Gale,et al. A sequential algorithm for training text classifiers , 1994, SIGIR '94.
[19] J. Klump,et al. Exploring prediction uncertainty of spatial data in geostatistical and machine learning approaches , 2019, Environmental Earth Sciences.
[20] Edzer Pebesma,et al. Stationary Sampling Designs Based on Plume Simulations , 2012 .
[21] D. J. Brus,et al. Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil (with discussion) , 1997 .
[22] Jorge Mateu,et al. Collecting spatio-temporal data , 2012 .
[23] Gerard B. M. Heuvelink,et al. Sampling desigh optimization for space-time kriging , 2012 .
[24] Devis Tuia,et al. Active learning for monitoring network optimization , 2012 .
[25] R. Koenker,et al. Regression Quantiles , 2007 .
[26] Marvin N. Wright,et al. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables , 2018, PeerJ.
[27] Paul M. Thompson,et al. Phenological sensitivity to climate across taxa and trophic levels , 2016, Nature.
[28] Long Ye,et al. Projecting Australia's forest cover dynamics and exploring influential factors using deep learning , 2019, Environ. Model. Softw..
[29] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[30] Benjamin Van Roy,et al. Deep Exploration via Bootstrapped DQN , 2016, NIPS.
[31] Sue J. Welham,et al. Environmental change and the phenology of European aphids , 2007 .
[32] Werner G. Müller,et al. Collecting Spatial Data: Optimum Design of Experiments for Random Fields , 1998 .
[33] Sébastien Destercke,et al. Epistemic Uncertainty Sampling , 2019, DS.
[34] Alex Kendall,et al. Concrete Dropout , 2017, NIPS.
[35] Susan P. Worner,et al. Geographical location, climate and land use influences on the phenology and numbers of the aphid, Myzus persicae, in Europe , 2005 .
[36] B. Cade,et al. A gentle introduction to quantile regression for ecologists , 2003 .
[37] David Lopez-Paz,et al. Single-Model Uncertainties for Deep Learning , 2018, NeurIPS.
[38] T. Clutton‐Brock,et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments , 2010 .
[39] Concha Bielza,et al. A survey on multi‐output regression , 2015, WIREs Data Mining Knowl. Discov..
[40] P. Mermelstein,et al. Opposite Effects of mGluR1a and mGluR5 Activation on Nucleus Accumbens Medium Spiny Neuron Dendritic Spine Density , 2016, PloS one.
[41] Ottar N. Bjørnstad,et al. Local and regional climate variables driving spring phenology of tortricid pests: a 36 year study , 2019, Ecological Entomology.
[42] Albin Cassirer,et al. Randomized Prior Functions for Deep Reinforcement Learning , 2018, NeurIPS.
[43] Yannig Goude,et al. Fast Calibrated Additive Quantile Regression , 2017, Journal of the American Statistical Association.
[44] Ian P. Woiwod,et al. Effects of temperature on aphid phenology , 1995 .
[45] Sebastián Ventura,et al. Performing Multi-Target Regression via a Parameter Sharing-Based Deep Network , 2019, Int. J. Neural Syst..
[46] Alfonso P. Ramallo-González,et al. Creating extreme weather time series through a quantile regression ensemble , 2018, Environ. Model. Softw..
[47] Furno Marilena,et al. Quantile Regression , 2018, Wiley Series in Probability and Statistics.
[48] Jun Wang,et al. Optimization of a Coastal Environmental Monitoring Network Based on the Kriging Method: A Case Study of Quanzhou Bay, China , 2016, BioMed research international.
[49] Olivier Bonnefon,et al. Modelling Population Dynamics in Realistic Landscapes with Linear Elements: A Mechanistic-Statistical Reaction-Diffusion Approach , 2016, PloS one.
[50] G. Heuvelink,et al. Optimization of sample patterns for universal kriging of environmental variables , 2007 .
[51] A. Dixon,et al. The effect of plant drought‐stress on populations of the pea aphid Acyrthosiphon pisum , 2001 .
[52] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[53] Huu Tuan Do,et al. Design of sampling locations for mountainous river monitoring , 2012, Environ. Model. Softw..
[54] Edzer J. Pebesma,et al. Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network , 2009, Comput. Geosci..
[55] Sue J. Welham,et al. The trait and host plant ecology of aphids and their distribution and abundance in the United Kingdom , 2012 .
[56] Gunter Spöck,et al. Spatial sampling design based on spectral approximations to the random field , 2012, Environ. Model. Softw..
[57] A. Comrie. Comparing Neural Networks and Regression Models for Ozone Forecasting , 1997 .
[58] Jorge Mateu,et al. Spatio-Temporal Design: Advances in Efficient Data Acquisition , 2012 .