Journal ArticleDOI
Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time
TLDR
iST-RF can improve predictive accuracy compared to the aspatial RF approach while enhancing interpretations of the trained model’s spatio-temporal relevance for its ensemble prediction and can help balance prediction and interpretation with fidelity in a spatial data science life cycle.Abstract:
Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain them. Methodolo...read more
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A New Method for Quantitative Analysis of Driving Factors for Vegetation Coverage Change in Mining Areas: GWDF-ANN
Jian Li,Tingting Qin,Chengye Zhang,Huiyu Zheng,Junting Guo,Hui-Cheng Xie,Caiyue Zhang,Yicong Zhang +7 more
TL;DR: Zhang et al. as mentioned in this paper constructed 50 sets of geographically weighted artificial neural network models for fractional vegetation coverage (FVC) and its driving factors in the Shengli Coalfield.
Journal ArticleDOI
A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests
TL;DR: In this paper , an advanced geospatial analytics algorithm that improves the prediction power of a random forest regression model while addressing the issue of spatial dependence commonly found in geographical data is presented.
Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning (Core Science Keynote)
TL;DR: This paper synthesizes multiple methods for machine learning model interpretation and visualization (MIV) focusing on meteorological applications, and concludes that ML has recently exploded in popularit...
Journal ArticleDOI
Topography, Climate and Fire History Regulate Wildfire Activity in the Alaskan Tundra
TL;DR: This paper investigated the relative influence of fire history, climate, topography and vegetation on fire occurrence and size in Alaskan tundra (1981-2019) and the potential for self-reinforcing/limiting fire behavior.
Journal ArticleDOI
Statistical mechanics in climate emulation: Challenges and perspectives
TL;DR: In this paper , the authors discuss how climate emulators rooted in statistical mechanics and machine learning can give rise to new climate models that are more reliable and require less observational and computational resources.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Journal ArticleDOI
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Journal ArticleDOI
A Computer Movie Simulating Urban Growth in the Detroit Region
TL;DR: A Computer Movie Simulating Urban Growth in the Detroit Region as discussed by the authors was made to simulate urban growth in the city of Detroit, Michigan, United States of America, 1970, 1970.
Posted Content
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez,Been Kim +1 more
TL;DR: This position paper defines interpretability and describes when interpretability is needed (and when it is not), and suggests a taxonomy for rigorous evaluation and exposes open questions towards a more rigorous science of interpretable machine learning.