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Evaluation of Explainable Ai Models in Analysis of Transferable Features of Geothermal Indicators

https://doi.org/10.2139/ssrn.4897692
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inferredcites →https://doi.org/10.1007/978-3-030-28954-6_1openalex-snapshot
Towards Explainable Artificial Intelligence
inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot
Visualizing and Understanding Convolutional Networks
inferredcites →https://doi.org/10.1016/j.cageo.2022.105227openalex-snapshot
Magnetic anomalies characterization: Deep learning and explainability
inferredcites →https://doi.org/10.1016/j.cageo.2022.105281openalex-snapshot
Karstified zone interpretation using deep learning algorithms: Convolutional neural networks applications and model interpretability with explainable AI
inferredcites →https://doi.org/10.1016/j.geothermics.2014.09.002openalex-snapshot
Remote sensing of geothermal-related minerals for resource exploration in Nevada
inferredcites →https://doi.org/10.1016/j.patcog.2016.11.008openalex-snapshot
Explaining nonlinear classification decisions with deep Taylor decomposition
inferredcites →https://doi.org/10.1016/j.renene.2022.04.113openalex-snapshot
The Geothermal Artificial Intelligence for geothermal exploration
inferredcites →https://doi.org/10.1016/j.rse.2006.05.005openalex-snapshot
Geothermal exploration with Hymap hyperspectral data at Brady–Desert Peak, Nevada
inferredcites →https://doi.org/10.1017/eds.2022.7openalex-snapshot
Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset
inferredcites →https://doi.org/10.1073/pnas.2015759118openalex-snapshot
Replication across space and time must be weak in the social and environmental sciences
inferredcites →https://doi.org/10.1080/13658816.2020.1805116openalex-snapshot
A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence
inferredcites →https://doi.org/10.1111/tgis.12996openalex-snapshot
Explainable artificial intelligence in the spatial domain ( X‐GeoAI )
inferredcites →https://doi.org/10.1145/3557918.3565869openalex-snapshot
Real-time GeoAI for high-resolution mapping and segmentation of arctic permafrost features
inferredcites →https://doi.org/10.1371/journal.pone.0130140openalex-snapshot
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
inferredcites →https://doi.org/10.1615/jmachlearnmodelcomput.2022046445openalex-snapshot
GEOTHERMALCLOUD: MACHINE LEARNING FOR GEOTHERMAL RESOURCE EXPLORATION
inferredcites →https://doi.org/10.3390/en17133255openalex-snapshot
An Evaluation of AI Models’ Performance for Three Geothermal Sites
inferredcites →https://doi.org/10.3390/ijerph192315926openalex-snapshot
Examining the Relationship between Land Use/Land Cover (LULC) and Land Surface Temperature (LST) Using Explainable Artificial Intelligence (XAI) Models: A Case Study of Seoul, South Korea
inferredcites →https://doi.org/10.3390/ijgi11070385openalex-snapshot
GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography
inferredcites →https://doi.org/10.3390/ijgi11080422openalex-snapshot
Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach
inferredcites →https://doi.org/10.3390/rs14133074openalex-snapshot
Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing
inferredcites →https://doi.org/10.3390/su16104235openalex-snapshot
Optimizing Residential Construction Site Selection in Mountainous Regions Using Geospatial Data and eXplainable AI
inferredcites →https://doi.org/10.48550/arxiv.0912.1128openalex-snapshot
How to Explain Individual Classification Decisions
inferredcites →https://doi.org/10.48550/arxiv.1412.6806openalex-snapshot
Striving for Simplicity: The All Convolutional Net
inferredcites →https://doi.org/10.48550/arxiv.1705.05598openalex-snapshot
Learning how to explain neural networks: PatternNet and PatternAttribution
inferredcites →https://doi.org/10.5311/josis.2020.20.658openalex-snapshot
GeoAI: Where machine learning and big data converge in GIScience
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