SPOTting Model Parameters Using a Ready-Made Python Package.
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 optim...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0145180&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850189124999315456 |
|---|---|
| author | Tobias Houska Philipp Kraft Alejandro Chamorro-Chavez Lutz Breuer |
| author_facet | Tobias Houska Philipp Kraft Alejandro Chamorro-Chavez Lutz Breuer |
| author_sort | Tobias Houska |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-1aea00cd8d87435d8206f067df700e8c |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1aea00cd8d87435d8206f067df700e8c2025-08-20T02:15:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-011012e014518010.1371/journal.pone.0145180SPOTting Model Parameters Using a Ready-Made Python Package.Tobias HouskaPhilipp KraftAlejandro Chamorro-ChavezLutz BreuerThe 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0145180&type=printable |
| spellingShingle | Tobias Houska Philipp Kraft Alejandro Chamorro-Chavez Lutz Breuer SPOTting Model Parameters Using a Ready-Made Python Package. PLoS ONE |
| title | SPOTting Model Parameters Using a Ready-Made Python Package. |
| title_full | SPOTting Model Parameters Using a Ready-Made Python Package. |
| title_fullStr | SPOTting Model Parameters Using a Ready-Made Python Package. |
| title_full_unstemmed | SPOTting Model Parameters Using a Ready-Made Python Package. |
| title_short | SPOTting Model Parameters Using a Ready-Made Python Package. |
| title_sort | spotting model parameters using a ready made python package |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0145180&type=printable |
| work_keys_str_mv | AT tobiashouska spottingmodelparametersusingareadymadepythonpackage AT philippkraft spottingmodelparametersusingareadymadepythonpackage AT alejandrochamorrochavez spottingmodelparametersusingareadymadepythonpackage AT lutzbreuer spottingmodelparametersusingareadymadepythonpackage |