On the stationary state of Kohonen's self-organizing sensory mapping
Biological Cybernetics
Machine Learning
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Environmental Modelling & Software
Introductory Time Series with R
Introductory Time Series with R
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
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Position paper: Characterising performance of environmental models
Environmental Modelling & Software
Forecasting ENSO with a smooth transition autoregressive model
Environmental Modelling & Software
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A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009-2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.