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An illustration of model agnostic explainability methods applied to environmental data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/asmb.446openalex-snapshot
Analysis of regression in game theory approach
inferredcites →https://doi.org/10.1002/env.2374openalex-snapshot
A model‐based approach for analog spatio‐temporal dynamic forecasting
inferredcites →https://doi.org/10.1002/env.2553openalex-snapshot
Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting
inferredcites →https://doi.org/10.1007/978-1-60327-101-1_2openalex-snapshot
Overview of Artificial Neural Networks
inferredcites →https://doi.org/10.1007/978-3-030-57321-8_2openalex-snapshot
The Explanation Game: Explaining Machine Learning Models Using Shapley Values
inferredcites →https://doi.org/10.1007/978-3-642-46466-9_18openalex-snapshot
Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition
inferredcites →https://doi.org/10.1007/bfb0006203openalex-snapshot
Applications of advances in nonlinear sensitivity analysis
inferredcites →https://doi.org/10.1007/s10115-013-0679-xopenalex-snapshot
Explaining prediction models and individual predictions with feature contributions
inferredcites →https://doi.org/10.1007/s10822-020-00314-0openalex-snapshot
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
inferredcites →https://doi.org/10.1007/s11053-019-09492-7openalex-snapshot
Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost
inferredcites →https://doi.org/10.1016/j.artint.2021.103502openalex-snapshot
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
inferredcites →https://doi.org/10.1016/j.atmosenv.2018.07.058openalex-snapshot
Spatial estimation of urban air pollution with the use of artificial neural network models
inferredcites →https://doi.org/10.1016/j.compstruct.2022.115184openalex-snapshot
Long-term performance prediction framework based on XGBoost decision tree for pultruded FRP composites exposed to water, humidity and alkaline solution
inferredcites →https://doi.org/10.1016/j.envsoft.2019.06.014openalex-snapshot
A review of artificial neural network models for ambient air pollution prediction
inferredcites →https://doi.org/10.1016/j.envsoft.2019.104567openalex-snapshot
Ensemble method based on Artificial Neural Networks to estimate air pollution health risks
inferredcites →https://doi.org/10.1016/j.eswa.2021.114832openalex-snapshot
Identifying mortality factors from Machine Learning using Shapley values – a case of COVID19
inferredcites →https://doi.org/10.1016/j.inffus.2021.05.008openalex-snapshot
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.12.010openalex-snapshot
Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
inferredcites →https://doi.org/10.1016/j.jenvman.2021.112719openalex-snapshot
An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates
inferredcites →https://doi.org/10.1016/j.jhydrol.2020.125033openalex-snapshot
Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)
inferredcites →https://doi.org/10.1016/j.jhydrol.2021.126382openalex-snapshot
XGBoost-based method for flash flood risk assessment
inferredcites →https://doi.org/10.1016/j.renene.2010.01.029openalex-snapshot
Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment
inferredcites →https://doi.org/10.1016/s0304-3800(02)00257-0openalex-snapshot
Review and comparison of methods to study the contribution of variables in artificial neural network models
inferredcites →https://doi.org/10.1016/s0304-3800(96)00049-xopenalex-snapshot
Artificial neural network approach for modelling and prediction of algal blooms
inferredcites →https://doi.org/10.1021/acs.est.1c01339openalex-snapshot
Machine Learning: New Ideas and Tools in Environmental Science and Engineering
inferredcites →https://doi.org/10.1021/acs.estlett.9b00476openalex-snapshot
A Machine Learning Approach for Predicting Defluorination of Per- and Polyfluoroalkyl Substances (PFAS) for Their Efficient Treatment and Removal
inferredcites →https://doi.org/10.1021/acs.jmedchem.9b01101openalex-snapshot
Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values
inferredcites →https://doi.org/10.1029/2003jd004345openalex-snapshot
Climate Prediction Center global monthly soil moisture data set at 0.5° resolution for 1948 to present
inferredcites →https://doi.org/10.1029/2018wr024090openalex-snapshot
Improving Precipitation Estimation Using Convolutional Neural Network
inferredcites →https://doi.org/10.1037/h0042519openalex-snapshot
The perceptron: A probabilistic model for information storage and organization in the brain.
inferredcites →https://doi.org/10.1038/s41586-019-0912-1openalex-snapshot
Deep learning and process understanding for data-driven Earth system science
inferredcites →https://doi.org/10.1038/s41598-021-91253-9openalex-snapshot
PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network
inferredcites →https://doi.org/10.1057/s41272-020-00236-4openalex-snapshot
Interpretable machine learning for demand modeling with high-dimensional data using Gradient Boosting Machines and Shapley values
inferredcites →https://doi.org/10.1080/0094965031000136012openalex-snapshot
A new partitioning around medoids algorithm
inferredcites →https://doi.org/10.1109/29.21701openalex-snapshot
Phoneme recognition using time-delay neural networks
inferredcites →https://doi.org/10.1109/access.2020.3040166openalex-snapshot
Explainable Prediction of Acute Myocardial Infarction Using Machine Learning and Shapley Values
inferredcites →https://doi.org/10.1109/access.2021.3112397openalex-snapshot
Explainable Unsupervised Machine Learning for Cyber-Physical Systems
inferredcites →https://doi.org/10.1109/cvpr.2012.6248110openalex-snapshot
Multi-column deep neural networks for image classification
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00781openalex-snapshot
Diversified Arbitrary Style Transfer via Deep Feature Perturbation
inferredcites →https://doi.org/10.1109/icdar.2005.251openalex-snapshot
Using GPUs for machine learning algorithms
inferredcites →https://doi.org/10.1109/jproc.2021.3060483openalex-snapshot
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
inferredcites →https://doi.org/10.1109/tpami.2012.59openalex-snapshot
3D Convolutional Neural Networks for Human Action Recognition
inferredcites →https://doi.org/10.1111/2041-210x.12146openalex-snapshot
Advancing population ecology with integral projection models: a practical guide
inferredcites →https://doi.org/10.1111/ecog.05360openalex-snapshot
Explainable artificial intelligence enhances the ecological interpretability of black‐box species distribution models
inferredcites →https://doi.org/10.1111/j.1467-9892.2011.00765.xopenalex-snapshot
Statistics for Spatio‐Temporal Data
inferredcites →https://doi.org/10.1113/jphysiol.1968.sp008455openalex-snapshot
Receptive fields and functional architecture of monkey striate cortex
inferredcites →https://doi.org/10.1126/science.248.4957.904openalex-snapshot
Geophysical Interplays: El Niño, La Niña, and the Southern Oscillation . S. George Philander. Academic Press, San Diego, CA, 1989. x, 293 pp., illus. $59.50. International Geophysics Series, vol. 46
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1155/2020/8627824openalex-snapshot
Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data
inferredcites →https://doi.org/10.1175/1520-0442(1993)006<1067:ponsst>2.0.co;2openalex-snapshot
Prediction of Niño 3 Sea Surface Temperatures Using Linear Inverse Modeling
inferredcites →https://doi.org/10.1175/1520-0477(1999)080<0217:psosad>2.0.co;2openalex-snapshot
Predictive Skill of Statistical and Dynamical Climate Models in SST Forecasts during the 1997—98 El Niño Episode and the 1998 La Niña Onset
inferredcites →https://doi.org/10.1175/2007jcli1578.1openalex-snapshot
Synoptic Circulation and Land Surface Influences on Convection in the Midwest U.S. “Corn Belt” during the Summers of 1999 and 2000. Part I: Composite Synoptic Environments
inferredcites →https://doi.org/10.1175/jcli-d-16-0836.1openalex-snapshot
Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons
inferredcites →https://doi.org/10.1175/jcli3420.1openalex-snapshot
Did the ECMWF Seasonal Forecast Model Outperform Statistical ENSO Forecast Models over the Last 15 Years?
inferredcites →https://doi.org/10.1186/s13071-021-04851-xopenalex-snapshot
Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso
inferredcites →https://doi.org/10.1201/9781351769723openalex-snapshot
Spatio-Temporal Statistics with R
inferredcites →https://doi.org/10.1214/20-sts783openalex-snapshot
A Selective Overview of Deep Learning
inferredcites →https://doi.org/10.1214/21-ss133openalex-snapshot
Interpretable machine learning: Fundamental principles and 10 grand challenges
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.1214/ss/1009213726openalex-snapshot
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
inferredcites →https://doi.org/10.12911/22998993/64828openalex-snapshot
APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF AIR POLLUTION LEVELS IN ENVIRONMENTAL MONITORING
inferredcites →https://doi.org/10.1364/ao.29.004790openalex-snapshot
Parallel distributed processing model with local space-invariant interconnections and its optical architecture
inferredcites →https://doi.org/10.1371/journal.pone.0248896openalex-snapshot
Adaptive dimensionality reduction for neural network-based online principal component analysis
inferredcites →https://doi.org/10.1515/9781400881970-018openalex-snapshot
17. A Value for n-Person Games
inferredcites →https://doi.org/10.21203/rs.3.rs-966026/v1openalex-snapshot
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
inferredcites →https://doi.org/10.21533/pen.v9.i4.997openalex-snapshot
Enhancement automatic speech recognition by deep neural networks
inferredcites →https://doi.org/10.3389/fpls.2019.01750openalex-snapshot
A CNN-RNN Framework for Crop Yield Prediction
inferredcites →https://doi.org/10.3390/agronomy11020282openalex-snapshot
Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression
inferredcites →https://doi.org/10.3390/en14248498openalex-snapshot
Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory
inferredcites →https://doi.org/10.4314/wsa.v34i2.183640openalex-snapshot
Prediction of groundwater levels from lake levels and climate data using ann approach
inferredcites →https://doi.org/10.48550/arxiv.1801.01489openalex-snapshot
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
inferredcites →https://doi.org/10.5194/gmd-14-6355-2021openalex-snapshot
Fast and accurate learned multiresolution dynamical downscaling for precipitation
inferredcites →https://doi.org/10.5194/hess-26-795-2022openalex-snapshot
Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling
inferredcites →https://doi.org/10.5281/zenodo.6353635openalex-snapshot
wesleyburr/explainableAI_Environmetrics: Version 0.9.0
inferredcites →https://doi.org/10.5281/zenodo.6353970openalex-snapshot
Soil Moisture and Sea Surface Temperature Data for Wikle et al. (2022)
inferredcites →https://doi.org/10.5555/1756006.1756007openalex-snapshot
An Efficient Explanation of Individual Classifications using Game Theory
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/ecs2.4751openalex-snapshot
Evidence from interpretable machine learning to inform spatial management of Palau's tuna fisheries
inferred← citeshttps://doi.org/10.1002/env.2855openalex-snapshot
Catalysing virtual collaboration: The experience of the remote TIES working groups
inferred← citeshttps://doi.org/10.1002/env.2864openalex-snapshot
Assessing predictability of environmental time series with statistical and machine learning models
inferred← citeshttps://doi.org/10.1002/widm.70075openalex-snapshot
Interpretable Machine Learning: A Comprehensive Review of Foundations, Methods, and the Path Forward
inferred← citeshttps://doi.org/10.1007/978-3-031-69499-8_8openalex-snapshot
Shallow Learning Versus Deep Learning in Natural Language Processing Applications
inferred← citeshttps://doi.org/10.1007/978-981-95-3767-9_10openalex-snapshot
Challenges and Risks to the Inclusion of AI for Environmental Applications
inferred← citeshttps://doi.org/10.1007/s00704-025-05507-xopenalex-snapshot
Improving the prediction of global solar radiation using interpretable boosting algorithms coupled SHAP and LIME analysis: a comparative study
inferred← citeshttps://doi.org/10.1007/s11222-025-10656-0openalex-snapshot
S-SIRUS: an explainability algorithm for spatial regression Random Forest
inferred← citeshttps://doi.org/10.1007/s11269-025-04401-5openalex-snapshot
A Comparative Evaluation of Deep Learning and Machine Learning Models for River Suspended Sediment Concentration Forecasting
inferred← citeshttps://doi.org/10.1007/s11269-025-04435-9openalex-snapshot
Explainable Artificial Intelligence in Hydrology: A Review
inferred← citeshttps://doi.org/10.1007/s12145-025-01782-4openalex-snapshot
Advancing forest fire prediction: A multi-layer stacking ensemble model approach
inferred← citeshttps://doi.org/10.1007/s13253-025-00691-9openalex-snapshot
Plant Disease Detection with Transfer Learning: Evaluating the Impact of Various Loss Functions and Explainable AI
inferred← citeshttps://doi.org/10.1016/j.ecohyd.2025.100697openalex-snapshot
Interpretable ensemble learning for modeling phosphorus dynamics and mitigating eutrophication in riverine ecosystems
inferred← citeshttps://doi.org/10.1016/j.ecolind.2026.115445openalex-snapshot
Explainable machine learning and causal inference reveal habitat-specific environmental and functional-trait drivers of zooplankton biodiversity in a karst river–reservoir system
inferred← citeshttps://doi.org/10.1016/j.fishres.2025.107468openalex-snapshot
Interpretable fish abundance index prediction in tuna longline fisheries: A LightGBM-SHAP case study in the tropical Atlantic Ocean
inferred← citeshttps://doi.org/10.1016/j.oceaneng.2026.125763openalex-snapshot
A gradient boosted mixed-model machine learning framework for vessel speed in the U.S. Arctic
inferred← citeshttps://doi.org/10.1016/j.spasta.2023.100732openalex-snapshot
Using echo state networks to inform physical models for fire front propagation
inferred← citeshttps://doi.org/10.1016/j.spasta.2024.100811openalex-snapshot
Deep graphical regression for jointly moderate and extreme Australian wildfires
inferred← citeshttps://doi.org/10.1080/26941899.2025.2565243openalex-snapshot
Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments
inferred← citeshttps://doi.org/10.1093/jrsssb/qkae118openalex-snapshot
Spatial effect detection regression for large-scale spatio-temporal covariates
inferred← citeshttps://doi.org/10.1111/ppl.70611openalex-snapshot
AI‐Driven Omics for Smart Remediation of Heavy Metal Contaminated Soils
inferred← citeshttps://doi.org/10.11606/t.18.2023.tde-18042024-141528openalex-snapshot
Development of index insurance under the dynamics of multi-hazard risk in the context of climate change
inferred← citeshttps://doi.org/10.1371/journal.pone.0303632openalex-snapshot
Child mental health predictors among camp Tamil refugees: Utilizing linear and XGBOOST models
inferred← citeshttps://doi.org/10.2139/ssrn.7206689openalex-snapshot
Generative Inpainting as a Diagnostic Tool for Marine Wildlife Detection Robustness
inferred← citeshttps://doi.org/10.3390/toxics14040335openalex-snapshot
Explainable Deep Learning for Research on the Synergistic Mechanisms of Multiple Pollutants: A Critical Review
inferred← citeshttps://doi.org/10.4018/979-8-3693-9045-0.ch005openalex-snapshot
Explainable AI in Healthcare Imaging for Medical Diagnosis
inferred← citeshttps://doi.org/10.5194/egusphere-2023-3002-rc1openalex-snapshot
Comment on egusphere-2023-3002
inferred← citeshttps://doi.org/10.5194/egusphere-2023-3002-rc2openalex-snapshot
Comment on egusphere-2023-3002
inferred← citeshttps://doi.org/10.5194/nhess-25-1387-2025openalex-snapshot
A data-driven framework for assessing climatic impact drivers in the context of food security
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