Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction | Paper Citation Record

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This paper cites and Negri, Jacquelyn A.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Negri, Jacquelyn A

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This paper cites and Addison, Priscilla and Oommen, Thomas and Salazar, Sean E.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Addison, Priscilla and Oommen, Thomas and Salazar, Sean E

Reference 2

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This paper cites and Negri, Jacquelyn A.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Negri, Jacquelyn A

Reference 3

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This paper cites and Destro, Elisa and Bhuiyan, Md Abul Ehsan and Borga, Marco and Anagnostou, Emmanouil N.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Destro, Elisa and Bhuiyan, Md Abul Ehsan and Borga, Marco and Anagnostou, Emmanouil N

Reference 4

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This paper cites Machine Learning for Improved Post-fire Debris Flow Likelihood Prediction , url =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Machine Learning for Improved Post-fire Debris Flow Likelihood Prediction , url =

Reference 5

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This paper cites and Gartner, Joseph E.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Gartner, Joseph E

Reference 6

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This paper cites TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models

Reference 7

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This paper cites Accurate predictions on small data with a tabular foundation model , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Accurate predictions on small data with a tabular foundation model , volume =

Reference 8

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This paper cites TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 9

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This paper cites xRFM: Accurate, scalable, and interpretable feature learning models for tabular data.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction xRFM: Accurate, scalable, and interpretable feature learning models for tabular data

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This paper cites Landslide Risk Assessment as a Reference for Disaster Prevention and Mitigation: A Case Study of the Renhe District, Panzhihua City, China , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Landslide Risk Assessment as a Reference for Disaster Prevention and Mitigation: A Case Study of the Renhe District, Panzhihua City, China , volume =

Reference 11

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This paper cites Assessment of post-wildfire debris flow occurrence using classifier tree , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Assessment of post-wildfire debris flow occurrence using classifier tree , volume =

Reference 12

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This paper cites Interpretable Machine Learning for TabPFN.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Interpretable Machine Learning for TabPFN

Reference 13

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This paper cites Transformers Can Do Bayesian Inference.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Transformers Can Do Bayesian Inference

Reference 14

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This paper cites and Kean, Jason W.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Kean, Jason W

Reference 15

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This paper cites and Benson, Nathan C.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Benson, Nathan C

Reference 16

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This paper cites A Unified Approach to Interpreting Model Predictions.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction A Unified Approach to Interpreting Model Predictions

Reference 17

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This paper cites A method for better mapping of susceptibility to thaw hazards in data-scarce cold regions , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction A method for better mapping of susceptibility to thaw hazards in data-scarce cold regions , volume =

Reference 18

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This paper cites Reliability and effectiveness of early warning systems for natural hazards: Concept and application to debris flow warning , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Reliability and effectiveness of early warning systems for natural hazards: Concept and application to debris flow warning , volume =

Reference 19

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This paper cites Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model , url =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model , url =

Reference 20

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This paper cites Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective , volume =

Reference 21

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This paper cites Exploring the Application of a Debris Flow Likelihood Regression Model in Mediterranean Post-Fire Environments, Using Field Observations-Based Validation , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Exploring the Application of a Debris Flow Likelihood Regression Model in Mediterranean Post-Fire Environments, Using Field Observations-Based Validation , volume =

Reference 22

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This paper cites Addressing class imbalance in soil movement predictions , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Addressing class imbalance in soil movement predictions , volume =

Reference 23

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This paper cites Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using

Reference 24

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction The meaning and use of the area under a receiver operating characteristic (

Reference 25

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This paper cites Proceedings of the 22nd.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Proceedings of the 22nd

Reference 26

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This paper cites A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data , volume =

Reference 27

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Understanding variable importances in forests of randomized trees , volume =

Reference 28

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Random Forests , volume =

Reference 29

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This paper cites A comparative study of different classification techniques for marine oil spill identification using.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction A comparative study of different classification techniques for marine oil spill identification using

Reference 30

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Unresolved cited work

Reference 31

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction , urldate =

Reference 32

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction , urldate =

Reference 33

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Bottou, L

Reference 34

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Optuna: A Next-generation Hyperparameter Optimization Framework , isbn =

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization , volume =

Reference 36

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Visualizing Data using t-

Reference 37

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Unresolved cited work

Reference 38

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction The Wasserstein distances , isbn =

Reference 39

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Hart, P

Reference 40

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Support-vector networks , volume =

Reference 41

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Extremely randomized trees , volume =

Reference 42

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Hinton, Geoffrey E

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Rizzo, Maria L

Reference 44

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This paper cites Why do tree-based models still outperform deep learning on tabular data?.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Why do tree-based models still outperform deep learning on tabular data?

Reference 45

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Deep Neural Networks and Tabular Data: A Survey , volume =

Reference 46

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Modeling Tabular data using Conditional GAN

Reference 47

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This paper cites Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis , volume =

Reference 48

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction doi:10.2113/gseegeosci.21.4.277 , abstract =

Reference 49

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Language Models are Few-Shot Learners , volume =

Reference 50

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction The Elements of Statistical Learning , rights =

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 52

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction A survey of cross-validation procedures for model selection , volume =

Reference 53

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction A study of cross-validation and bootstrap for accuracy estimation and model selection , isbn =

Reference 54

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This paper cites and Alexander, R.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Alexander, R

Reference 55

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction and Talbot, Nicola L.C

Reference 56

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This paper cites A mathematical framework for studying rainfall intensity-duration-frequency relationships , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction A mathematical framework for studying rainfall intensity-duration-frequency relationships , volume =

Reference 57

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Unresolved cited work

Reference 58

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction , urldate =

Reference 59

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This paper cites pfdf - Python library for postfire debris-flow hazard assessments and research, version 3.0.2 , url =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction pfdf - Python library for postfire debris-flow hazard assessments and research, version 3.0.2 , url =

Reference 60

Resolution: verified exact; doi, observed 2026-08-08T16:49:25.146227Z

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This paper cites "Why Should I Trust You?": Explaining the Predictions of Any Classifier.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction "Why Should I Trust You?": Explaining the Predictions of Any Classifier

Reference 61

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Explainable AI for Trees: From Local Explanations to Global Understanding

Reference 62

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Statistical Comparisons of Classifiers over Multiple Data Sets , volume =

Reference 63

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Long short-term memory

Reference 64

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction , urldate =

Reference 65

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Cross-validation pitfalls when selecting and assessing regression and classification models , volume =

Reference 66

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This paper cites Attention is All you Need , volume =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Attention is All you Need , volume =

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:26.620752Z

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This paper cites Advances in Neural Information Processing Systems , publisher =.

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Advances in Neural Information Processing Systems , publisher =

Reference 68

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction , urldate =

Reference 69

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis , volume =

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:26.590804Z

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Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction Inference for the Generalization Error , volume =

Reference 71

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