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Optimizing the decomposition of time series using evolutionary algorithms: soil moisture analytics

Published: 01 July 2017 Publication History

Abstract

Soil moisture plays a crucial part in earth science, with impact on agriculture, ecology, hydrology, landslides, and water resources. Extremes in soil moisture, which we denote as peaks and valleys, caused by heavy rainfalls and subsequent dry weather, are very important when predicting future soil moisture or even landslides. Existing methods, like moving averages, have limitations when it comes to smoothing time series data while preserving peaks and valleys. In this work, we propose a novel method, HyperSTL, for extrema-preserving smoothing of soil moisture time series. The method optimizes an existing time series decomposition technique, Seasonal Decomposition of Time Series by Loess (STL). HyperSTL optimizes STL's control parameters, which we call hyperparameters, using an objective function over the decomposed components. We demonstrate in experiments with nine soil moisture datasets that using HyperSTL generally results in improved predictions compared to using other smoothing methods.

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  • (2024)Soil moisture forecasting from sensors-based soil moisture, weather and irrigation observations: A systematic reviewSmart Agricultural Technology10.1016/j.atech.2024.100692(100692)Online publication date: Dec-2024
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  • (2023)When good signatures go bad: Applying hydrologic signatures in large sample studiesHydrological Processes10.1002/hyp.1498737:9Online publication date: 15-Sep-2023
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cover image ACM Conferences
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference
July 2017
1427 pages
ISBN:9781450349208
DOI:10.1145/3071178
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 01 July 2017

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Author Tags

  1. STL
  2. smoothing
  3. smoothing spline
  4. soil moisture
  5. stochastic optimization
  6. time series decomposition

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GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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  • (2023)Spatiotemporal Analysis of Long-Term Rainfall in Semi-Arid Area Using Artificial Intelligence Models (Case Study: Ilam Province, Iran)Water10.3390/w1519352115:19(3521)Online publication date: 9-Oct-2023
  • (2023)When good signatures go bad: Applying hydrologic signatures in large sample studiesHydrological Processes10.1002/hyp.1498737:9Online publication date: 15-Sep-2023
  • (2022)Multitemporal dual-pol Sentinel-1 data to support monitoring of forest post-fire dynamicsGeocarto International10.1080/10106049.2022.209838837:27(15463-15484)Online publication date: 11-Jul-2022
  • (2022)From data to interpretable models: machine learning for soil moisture forecastingInternational Journal of Data Science and Analytics10.1007/s41060-022-00347-815:1(9-32)Online publication date: 31-Aug-2022
  • (2022)A signature‐based approach to quantify soil moisture dynamics under contrasting land‐usesHydrological Processes10.1002/hyp.1455336:4Online publication date: 6-Apr-2022
  • (2021)Review of Swarm Intelligence for Improving Time Series ForecastingApplied Optimization and Swarm Intelligence10.1007/978-981-16-0662-5_4(61-79)Online publication date: 18-May-2021
  • (2019)Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural NetworksJ10.3390/j20100062:1(65-83)Online publication date: 14-Feb-2019

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