On the stationary state of Kohonen's self-organizing sensory mapping
Biological Cybernetics
Machine Learning
Environmental Modelling & Software
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty
Environmental Modelling & Software
Data-driven modeling of surface temperature anomaly and solar activity trends
Environmental Modelling & Software
Conceptual evaluation of continental land-surface model behaviour
Environmental Modelling & Software
Hybrid modeling of spatial continuity for application to numerical inverse problems
Environmental Modelling & Software
Environmental Modelling & Software
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Few studies attempt to model the range of possible post-fire hydrologic and geomorphic hazards because of the sparseness of data and the coupled, nonlinear, spatial, and temporal relationships among landscape variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 540 burned basins in the western United States. The sparsely populated data set includes variables from independent numerical landscape categories (climate, land surface form, geologic texture, and post-fire condition), independent landscape classes (bedrock geology and state), and dependent initiation processes (runoff, landslide, and runoff and landslide combination) and responses (debris flows, floods, and no events). Pattern analysis of the SOM-based component planes is used to identify and interpret relations among the variables. Application of the Davies-Bouldin criteria following k-means clustering of the SOM neurons identified eight conceptual regional models for focusing future research and empirical model development. A split-sample validation on 60 independent basins (not included in the training) indicates that simultaneous predictions of initiation process and response types are at least 78% accurate. As climate shifts from wet to dry conditions, forecasts across the burned landscape reveal a decreasing trend in the total number of debris flow, flood, and runoff events with considerable variability among individual basins. These findings suggest the SOM may be useful in forecasting real-time post-fire hazards, and long-term post-recovery processes and effects of climate change scenarios.