On Robustness of the Explanatory Power of Machine Learning Models: Insights From a New Explainable AI Approach Using Sensitivity Analysis

On Robustness of the Explanatory Power of Machine Learning Models: Insights From a New Explainable AI Approach Using Sensitivity Analysis

Abstract Machine learning (ML) is increasingly considered the solution to environmental problems where limited or no physico‐chemical process understanding exists. But in supporting high‐stakes decisions, where the ability to explain possible solutions is key to their acceptability and legitimacy, M...

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Bibliographic Details
Main Authors: Banamali Panigrahi, Saman Razavi, Lorne E. Doig, Blanchard Cordell, Hoshin V. Gupta, Karsten Liber
Format: Article
Language:English
Published: Wiley 2025-03-01
Series:Water Resources Research
Subjects:
machine learning
sensitivity analysis
feature selection
model randomness
water quality
Online Access:https://doi.org/10.1029/2024WR037398
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