Browsing by Subject "Weather forecasting"

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Open Access
Spatiotemporal series analysis and forecasting: new deep learning architectures on weather and crime forecasting
(2022-08) Tekin, Selim Furkan
We investigate spatiotemporal series through weather and crime forecasting and introduce new successful deep learning architectures that are also applicable to other spatiotemporal domains. First, we present our weather model being an alternative to physical models by addressing the computational cost and the performance. In our weather model, we extend the encoder-decoder structure with the Convolutional Long-short Term Memory units and enhance the performance with attention and context matcher mechanisms. We perform experiments on high-scale, real-life, benchmark numerical weather datasets. We show that attention matrices model atmospheric circulations and successfully capture spatial and temporal relations. Our model obtains the best scores among the baseline deep learning models and comparable results with the physical models. Secondly, we study high-resolution crime prediction and introduce a new generative model with Graph Convolutional Gated Recurrent Units (Graph-ConvGRU) and multivariate Gaussian distributions. We introduce a subdivision algorithm and create a graph representation to tackle the sparsity and complexity problem in high-resolution spatiotemporal data. By leveraging the flexible structure of graph representation, we encode the relations to state vectors for each region. We then create a multivariate probability distribution from the state vectors and perform prediction at any resolution for the first time in the literature. Our model obtains the best score in our experiments on real-life and synthetic datasets compared to the state-of-the-art models. Hence our model is not only generative but also precise. We also provide the source code of our algorithms for reproducibility.
Open Access
Supporting hurricane inventory management decisions with consumer demand estimates
(Elsevier B.V., 2016) Morrice, D. J.; Cronin, P.; Tanrisever, F.; Butler, J. C.
Matching supply and demand can be very challenging for anyone attempting to provide goods or services during the threat of a natural disaster. In this paper, we consider inventory allocation issues faced by a retailer during a hurricane event and provide insights that can be applied to humanitarian operations during slow-onset events. We start with an empirical analysis using regression that triangulates three sources of information: a large point-of-sales data set from a Texas Gulf Coast retailer, the retailer's operational and logistical constraints, and hurricane forecast data from the National Hurricane Center (NHC). We establish a strong association between the timing of the hurricane weather forecast, the forecasted landfall position of the storm, and hurricane sales. Storm intensity is found to have a weaker association on overall inventory decisions. Using the results of the empirical analysis and the NHC forecast data, we construct a state-space model of demand during the threat of a hurricane and develop an inventory management model to satisfy consumer demand prior to a hurricane making landfall. Based on the structure of the problem, we model this situation as a two-stage, two-location inventory allocation model from a centralized distribution center that balances transportation, shortage and holding costs. The model is used to explore the role of recourse, i.e., deferring part of the inventory allocation until observing the state of the hurricane as it moves towards landfall. Our approach provides valuable insights into the circumstances under which recourse may or may not be worthwhile in any setting where an anticipated extreme event drives consumer demand.