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SPOTPY

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hydrologybiogeochemicalvadose-zone1Dsite
SPOTPY

Tobias Houska1*, Philipp Kraft1, Alejandro Chamorro-Chavez1 and Lutz Breuer1,2

1 Institute for Landscape Ecology and Resources Management, Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus Liebig University, Giessen, Germany

2 Centre for International Development and Environmental Research, Justus Liebig University, Giessen, Germany

 

Website

http://www.uni-giessen.de/cms/faculties/f09/institutes/ilr/hydro/download/spotpy

 

Description

The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.

Available Algorithms:

Monte Carlo (MC), Markov-Chain Monte-Carlo (MCMC), Maximum Likelihood Estimation (MLE), Latin-Hypercube Sampling (LHS), Simulated Annealing (SA), Shuffled Complex Evolution Algorithm (SCE-UA), Differential Evolution Adaptive Metropolis Algorithm (DE-MCz), RObust Parameter Estimation (ROPE) and Fourier Amplitude Sensitivity Test (FAST).

 

Screen shots

 

Scientific articles

Houska, T., Kraft, P., Chamorro-Chavez, A. and Breuer, L.: SPOTting Model Parameters Using a Ready-Made Python Package, PLoS ONE, 10(12), e0145180, doi:10.1371/journal.pone.0145180, 2015.

 

Technical information

Operating system(s): all

Licence: MIT

Output(s): Sampled model parameters

Export format(s): csv-file

Other information: Suited for parallel computing

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