Print Email Facebook Twitter Breakpoint detection through neural nets Title Breakpoint detection through neural nets: A feasibility study Author Dijkstra, Fokke (TU Delft Civil Engineering and Geosciences) Contributor Lhermitte, Stef (mentor) Smal, Ihor (mentor) Steele-Dunne, Susan (mentor) Degree granting institution Delft University of Technology Programme Geoscience and Remote Sensing Date 2020-03-09 Abstract A variety of statistical methods are available to detect sudden changes, or breakpoints, in time series when used as multi-temporal change detection technique. However, these methods are unreliable in the presence of noise. Neural nets might detect breakpoints better. These deep learning models are able to generalize and optimize well, even in the presence of noise. This research tests the feasibility of different neural net architectures to detect breakpoints in generic linear time series. Two relatively simple neural nets are proposed, combined with four different descriptions of breakpoint, and trained on syntheticdata. The neural nets are tested on two datasets: On a separate synthetic dataset and on Australian rainuse-efficieny (RUE) time series, a surrogate for dryland ecosystem functioning. Some of the neural nets built performed exceptionally well on synthetic data, outperforming a benchmark statistical method withmargin. The direct translation to RUE time series was less successful. The results shows great promise for the use of neural nets in change detection. A generalist change detection approach by use of neural nets is likely not optimal. Current developments in deep learning, as well as choosing the right user-case, showgreat promise to unlock the full potential of neural nets in time series analysis. Subject breakpoint detectionmulti-temporal change detectiondeep learningneural nets To reference this document use: http://resolver.tudelft.nl/uuid:31e80f25-0e47-4e4e-99f0-26e936d43e90 Part of collection Student theses Document type master thesis Rights © 2020 Fokke Dijkstra Files PDF FDijkstra_MScThesis_Final.pdf 6.58 MB Close viewer /islandora/object/uuid:31e80f25-0e47-4e4e-99f0-26e936d43e90/datastream/OBJ/view