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A biologically based machine learning approach to tropical cyclone intensity forecasting
Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. (MDA)ORCID iD: 0000-0002-3997-1088
(English)Manuscript (preprint) (Other academic)
Abstract [en]

A biologically based ANN using four hierarchical levels, is trained and tested using temporal sequences of 2D inputs to forecast Tropical Cyclone (TC) intensity12, and 24 hours ahead in the Atlantic basin. We use five parallel input layers to feed infrared, ocean heat content, sea-level pressure, wind direction and wind speed images into the network. Forecasts are produced in the Saffir-Simpson hurricane intensity scale and are compared to the observed wind speeds in the TC best track data on two separate test datasets for validation. Forecasting accuracy is more than 95% for the test dataset containing temporal continuations of the TC lifecycle time-step images that are excluded from training, whereas, forecasting accuracy is between 30% and 55%, when images of a novel TC are used for testing. This result reveals that biologically inspired ANNs have a potential to be further developed into an effective TC intensity forecasting technique.

Keyword [en]
Biologically based artificial neural networks; bi-directionally connected networks; Markov chain; temporal sequence learning; simple recurrent networks
National Category
Other Computer and Information Science
URN: urn:nbn:se:liu:diva-123196OAI: diva2:877262
Available from: 2015-12-06 Created: 2015-12-06 Last updated: 2016-01-13Bibliographically approved
In thesis
1. An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting
Open this publication in new window or tab >>An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Introduction: Tropical Cyclones (TCs) inflict considerable damage to life and property every year. A major problem is that residents often hesitate to follow evacuation orders when the early warning messages are perceived as inaccurate or uninformative. The root problem is that providing accurate early forecasts can be difficult, especially in countries with less economic and technical means.

Aim: The aim of the thesis is to investigate how cyclone early warning systems can be technically improved. This means, first, identifying problems associated with the current cyclone early warning systems, and second, investigating if biologically based Artificial Neural Networks (ANNs) are feasible to solve some of the identified problems.

Method: First, for evaluating the efficiency of cyclone early warning systems, Bangladesh was selected as study area, where a questionnaire survey and an in-depth interview were administered. Second, a review of currently operational TC track forecasting techniques was conducted to gain a better understanding of various techniques’ prediction performance, data requirements, and computational resource requirements. Third, a technique using biologically based ANNs was developed to produce TC track and intensity forecasts. Systematic testing was used to find optimal values for simulation parameters, such as feature-detector receptive field size, the mixture of unsupervised and supervised learning, and learning rate schedule. Five types of 2D data were used for training. The networks were tested on two types of novel data, to assess their generalization performance.

Results: A major problem that is identified in the thesis is that the meteorologists at the Bangladesh Meteorological Department are currently not capable of providing accurate TC forecasts. This is an important contributing factor to residents’ reluctance to evacuate. To address this issue, an ANN-based TC track and intensity forecasting technique was developed that can produce early and accurate forecasts, uses freely available satellite images, and does not require extensive computational resources to run. Bidirectional connections, combined supervised and unsupervised learning, and a deep hierarchical structure assists the parallel extraction of useful features from five types of 2D data. The trained networks were tested on two types of novel data: First, tests were performed with novel data covering the end of the lifecycle of trained cyclones; for these test data, the forecasts produced by the networks were correct in 91-100% of the cases. Second, the networks were tested with data of a novel TC; in this case, the networks performed with between 30% and 45% accuracy (for intensity forecasts).

Conclusions: The ANN technique developed in this thesis could, with further extensions and up-scaling, using additional types of input images of a greater number of TCs, improve the efficiency of cyclone early warning systems in countries with less economic and technical means. The thesis work also creates opportunities for further research, where biologically based ANNs can be employed for general-purpose weather forecasting, as well as for forecasting other severe weather phenomena, such as thunderstorms.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University Electronic Press, 2016. 160 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1734
Biologically based artificial neural networks; bi-directionally connected networks; temporal sequence learning; accurate tropical cyclone forecasting; informative warning message;
National Category
Other Computer and Information Science
urn:nbn:se:liu:diva-123198 (URN)10.3384/diss.diva-123198 (DOI)978-91-7685-854-7 (Print) (ISBN)
Public defence
2016-02-17, Alan Turing, Hus E, Campus Valla, Linköping, 13:15 (English)
Available from: 2016-01-13 Created: 2015-12-06 Last updated: 2016-01-28Bibliographically approved

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