1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result;
Wetting and Drying of Soil: From Data to Understandable Models for Prediction
2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s);
Aniruddha Basak
206 5th Ave N, Apt 216, Seattle WA, 98109
aniruddha.digital@gmail.com
(+1) 650-391-5348
Ole J. Mengshoel
IT-bygget, Gløshaugen
ole.j.mengshoel@ntnu.no
+47 906 67 346
Kevin Schmidt
345 Middlefield Road MS 973 Menlo Park, CA 94025
kschmidt@usgs.gov
(+1) 650-329-5302
Chinmay Kulkarni
Carnegie Mellon University
chinmaymkulkarni@gmail.com
(+1) 650-564-7776
3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition);
Ole J. Mengshoel
4. the abstract of the paper(s);
Soil moisture is critical to agriculture, ecology, and certain natural disasters. Existing soil moisture models often fail to predict soil moisture accurately for time periods greater than a few hours. To tackle this problem, we introduce in this paper two novel models, the Naive Accumulative Representation (NAR) and the Additive Exponential Accumulative Representation (AEAR). The parameters in these models reflect hydrological redistribution processes of gravity and suction. We validate our models using soil moisture and rainfall time series data collected from a steep gradient post-wildfire site in Southern California. Data analysis is challenging, since rapid landscape change in steep, burned hillslopes is typically observed in response to even small to moderate rain events. We found that the AEAR model fits the data well for three distinct soil textures at different depths below the ground surface (at 5cm, 15cm, and 30cm). Similar strong results are demonstrated in controlled soil moisture experiments. Our recommended AEAR model has been validated as effective and useful by earth scientists, giving better forecasts than existing models for time horizons of 10 to 24 hours.
5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies;
The work satisfies the following criteria:
(D) The result is publishable in its own right as a new scientific result independent of the fact that the result was mechanically created.
(E) The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions.
6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission);
(D) We invented data driven models to predict soil moisture using information about current conditions and rainfall forecast. Our novel AEAR model resemble hydrological models of soil processes. The AEAR model fulfils three criterion: data-driven, understandable (i.e. model parameters can be easily interpreted from the earth science perspective, and clearly relate to or directly map to soil or hydrological properties), and accurate in predictions. We demonstrated the accuracy of the AEAR model in multiple soil moisture datasets collected from different sites.
(E) In the literature, The Antecedent Water Index (AWI) model is used to forecast soil moisture. It strikes the delicate balance of fitting soil moisture time-series data while providing meaningful information to geophysicists by expressing hydrologic parameters estimated from data. However, we establish in this paper that the AWI model is limited when predicting soil moisture for time horizons exceeding a few hours. Using inspirations from the AWI model and principles of statistical time series forecasting, we developed Additive Exponential Accumulative Representation (AEAR) model which accumulate rainfall over a time interval and introduces two different drying terms to represent the steep redistribution decay and gradual (lower) redistribution decay separately. This separation is particularly effective in modeling deeper soil where the soil moisture variations do not resemble an exponential curve. Therefore, we believe the AEAR model improves on the state-of-the-art human-created soil moisture models.
7. a full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable);
@INPROCEEDINGS{8631460,
author={A. {Basak} and O. J. {Mengshoel} and K. {Schmidt} and C. {Kulkarni}},
booktitle={2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)},
title={Wetting and Drying of Soil: From Data to Understandable Models for Prediction},
year={2018},
volume={},
number={},
pages={303-312},
keywords={data analysis;ecology;hydrological techniques;moisture;rain;soil;time series;wildfires;data analysis;soil moisture models;Naive Accumulative Representation;Additive Exponential Accumulative Representation;rainfall time series data;steep gradient post-wildfire site;soil textures;AEAR model;soil moisture experiments;soil wetting;soil drying;agriculture;ecology;gravity;Southern California;burned hillslopes;Soil moisture;Predictive models;Data models;Mathematical model;Soil measurements;Biological system modeling;soil moisture, earth science, time series, forecasting, exponential models, stochastic optimization, evolutionary algorithms},
doi={10.1109/DSAA.2018.00041},
ISSN={},
month={Oct},}
8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors;
Any prize money is to be split equally among the co-authors.
9. a statement stating why the authors expect that their entry would be the "best," and
Soil moisture plays an essential role on Earth, and due to climate change there may be surprising and impactful changes in soil moisture in the future. Our improved soil moisture model AEAR - developed with the help of evolutionary algorithm methods - both provides improved prediction (accuracy) while building on previous mathematical models used in the earth science community (explainability or interpretability). Moreover, we present a complete procedure of fitting the AEAR model to arbitrary soil moisture time-series starting from data pre-processing, parameter tuning using Differential Evolution, and interpreting the model parameters. The novel AEAR model can play a crucial role in early warning systems of extreme weather events like flash floods, or landslides caused by heavy rain.
10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc.
DE (differential evolution), GA (genetic algorithms)
11. The date of publication of each paper. If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is â€œin pressâ€ by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement.
August 01, 2018