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Groundwater Time Series Modeling Challenge

License: GNU General Public License v3.0

R 0.23% Jupyter Notebook 71.21% Python 9.25% HTML 19.25% TeX 0.01% MATLAB 0.05%
groundwater modeling time-series

challenge's Introduction

The Groundwater Modeling Challenge

Update 2022/09/19: Data is released and the challenge has started !

This repository contains all the information and materials for the Groundwater Time Series Modeling Challenge, as announced at the 2022 EGU General Assembly. We invite every interested groundwater modeler to model the five different hydraulic head time series found in the data folder, and send in their best possible simulated head time series.

Organisers: R.A. Collenteur (Eawag), E. Haaf (Chalmers), T. Liesch & A. Wunsch (KIT), and M. Bakker (TU Delft)

Background & Objectives

Different types of models can be applied to model groundwater level time series, ranging from purely statistical models (black-box), through lumped conceptual models (grey-box), to process-based models (white-box). Traditionally, physically based, distributed models are predominantly used to solve groundwater problems. In recent years, the use of grey- and black-box models has been receiving increased attention. With this challenge, we want to showcase the diversity of models that can be applied to solve groundwater problems, and systematically asses their differences and performances.

Input and hydraulic head data

Five hydraulic head time series were selected for this challenge. The monitoring wells are located in sedimentary aquifers, but in different climatological and hydrogeological settings. Depending on the location. different input time series are available to model the heads. Please find all data and descriptions in the data folder.

It is permitted to use any other publicly available data (e.g., soil maps) to construct the model. The use of other meteorological data that that provided is not permitted, to ensure that differences between the models are not the result of the meteorological input data. It is also not permitted to use the hydraulic heads as explanatory variables in the model.

Modeling rules

  • Participants may use any type of model.
  • The groundwater time series themselves may not be used as model input.
  • The modeling workflow must be reproducible, preferably through the use of scripts, but otherwise described in enough detail to reproduce the results.
  • Supplementary model data must be described in sufficient detail and submitted with model outputs.

Model outputs and deliverables

The model is expected to compute:

  • The prediction of the hydraulic head for the dates found in the submission files in the 'team_example' folder, including the 95% prediction interval of the hydraulic head at a daily time interval over the entire calibration and validation period (see data folders for specific periods for each location).

Forms that can be used to submit the results are provided in the submissions folder. There you can also find more detailed on what to submit.

Model evaluation

The models will be evaluated using several goodness-of-fit metrics and groundwater signatures, computed for both the calibration and the validation period. The data for the validation period is not make public yet and will be released after the challenge ended.

Deadline

The deadline for the challenge is 31/12/2022. Late submission are allowed untill 5th of January 24:00 CET. Please make sure to submit before this date. We plan to share the results of this challenge at the EGU General Assembly 2023.

Participation & Submission

If you intend to participate, please open a GitHub Issue for your team, such that we can track the participating teams.

Participant can submit their model results as a Pull Request to this Repository, adding a folder with their results in the 'submissions' folder. The model results must be submitted in a way that they are reproducible, either through the use of scripts (preferred) or detailed description of the modeling process. See the submissions folder for a more detailed description on how and what to submit.

After the challenge we intend to write an article to submit to a peer-reviewed journal with all the organisers and participants.

Questions/ Comments ?

To make sure everyone has access to the same information we ask you to put any questions that are of general interest to all participants in the GitHub Discussion forum.

challenge's People

Stargazers

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Watchers

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challenge's Issues

Team GEUS

Model: LSTM
Names: Raphael Schneider & Julian Koch

The DA collective

team members: Ed de Sousa, Rui Hugman, Mike Fienen, Nick Martin, Jeremy White
approach: ensemble of TFN models

missing data?

Im sure Im missing something but the forcing data csv files are constant in time - just one value for all times. Is that right? Like I said Im slow so maybe Im not understanding something...

Team HydroSight

Model: HydroSight - lumped conceptual model
Names: Xinyang Fan, Tim Peterson

Team Janis

Model: Random Forest model
Name: Jānis Bikše

I worked on this some time ago and was slow to polish it, but just noticed that Team Mirkwood has quite a similar approach. I hope it won't make any problems but definitely, it would interesting to compare the results.

Team TUV

Model: Transformer
Member: Anna Pölz, Ali Obeid, Ahmad Ameen

Team TUD

Model: LSTM
Names: Max Rudolph, Alireza Kavousi (both Institute of Groundwater Management, TU Dresden, Germany)

Team MxNl

Model: Ensemble of shallow learners
Names:

  • Max Nölscher

Team UW

Model: LSTM

Members:
Morteza Behbooei
Jimmy Lin
Rojin Meysami

Team Haidro

Model: multi-frequency LSTM with MC dropout for uncertainty estimates
Names: Tim Franken

Note: I'm a bit late with my subscription but still plan / hope to get the submission in before the deadline (5/1, 24hCET) if that's ok

Team Regression

Model: Linear Regression with Distributed Lags
Member: Jonathan Kennel

Team Example

Please open a GitHub Issue to register your team.

Model: XX-model
Names: X.X. XYZ

Team_RouhaniEtAl

Hi,

I just submitted my results for the challenge and submitted my results.

Model: 1D-CNN Deeplearning model
Team: AmirEtAl
please check and confirm if you have recieved.

Best,
Amir,

Team M2C CNRS & BRGM

Model: Hybrid deep-learning
Members

  1. Sivarama Krishna Reddy Chidepudi
  2. Abel Henriot
  3. Nicolas Massei
  4. Abderrahim Jardani

Team

Hello,

I'm planning to have a crack at this.

Matthew Taylor

Team Mirkwood

Model: Random forest ensemble
Name: Antoine Di Ciacca

Team LUHG

Model: NHiTS
Names: Nikolas Benavides Höglund

I'm a bit late to the game, but I would like to give it a chance and provide a contribution for this challenge. The model I'm using is an implementation of NHiTS (link to paper). LUHG = Lund University HydroGeology.

Team runwaygrey

Model: Mixed Effects Random Forest (MERF)
Names: Ayush Prasad (University of Helsinki)

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