Accessing the Impact of Meteorological Variables on Machine Learning Flood Susceptibility Mapping
Machine learning (ML) algorithms have emerged as competent tools for identifying areas that are susceptible to flooding. The primary variables considered in most of these works include terrain models, lithology, river networks and land use. While several recent studies include average annual rainfal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/7/1656 |
| _version_ | 1797437882170867712 |
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| author | Heather McGrath Piper Nora Gohl |
| author_facet | Heather McGrath Piper Nora Gohl |
| author_sort | Heather McGrath |
| collection | DOAJ |
| description | Machine learning (ML) algorithms have emerged as competent tools for identifying areas that are susceptible to flooding. The primary variables considered in most of these works include terrain models, lithology, river networks and land use. While several recent studies include average annual rainfall and/or temperature, other meteorological information such as snow accumulation and short-term intense rain events that may influence the hydrology of the area under investigation have not been considered. Notably, in Canada, most inland flooding occurs during the freshet, due to the melting of an accumulated snowpack coupled with heavy rainfall. Therefore, in this study the impact of several climate variables along with various hydro-geomorphological (HG) variables were tested to determine the impact of their inclusion. Three tests were run: only HG variables, the addition of annual average temperature and precipitation (HG-PT), and the inclusion of six other meteorological datasets (HG-8M) on five study areas across Canada. In HG-PT, both precipitation and temperature were selected as important in every study area, while in HG-8M a minimum of three meteorological datasets were considered important in each study area. Notably, as the meteorological variables were added, many of the initial HG variables were dropped from the selection set. The accuracy, F1, true skill and Area Under the Curve (AUC) were marginally improved when the meteorological data was added to the a parallel random forest algorithm (parRF). When the model is applied to new data, the estimated accuracy of the prediction is higher in HG-8M, indicating that inclusion of relevant, local meteorological datasets improves the result. |
| first_indexed | 2024-03-09T11:29:03Z |
| format | Article |
| id | doaj.art-faa11cba441c46019ca54c9f8c67709f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:29:03Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-faa11cba441c46019ca54c9f8c67709f2023-11-30T23:57:06ZengMDPI AGRemote Sensing2072-42922022-03-01147165610.3390/rs14071656Accessing the Impact of Meteorological Variables on Machine Learning Flood Susceptibility MappingHeather McGrath0Piper Nora Gohl1Natural Resources Canada, Ottawa, ON K1S 5K2, CanadaNatural Resources Canada, Ottawa, ON K1S 5K2, CanadaMachine learning (ML) algorithms have emerged as competent tools for identifying areas that are susceptible to flooding. The primary variables considered in most of these works include terrain models, lithology, river networks and land use. While several recent studies include average annual rainfall and/or temperature, other meteorological information such as snow accumulation and short-term intense rain events that may influence the hydrology of the area under investigation have not been considered. Notably, in Canada, most inland flooding occurs during the freshet, due to the melting of an accumulated snowpack coupled with heavy rainfall. Therefore, in this study the impact of several climate variables along with various hydro-geomorphological (HG) variables were tested to determine the impact of their inclusion. Three tests were run: only HG variables, the addition of annual average temperature and precipitation (HG-PT), and the inclusion of six other meteorological datasets (HG-8M) on five study areas across Canada. In HG-PT, both precipitation and temperature were selected as important in every study area, while in HG-8M a minimum of three meteorological datasets were considered important in each study area. Notably, as the meteorological variables were added, many of the initial HG variables were dropped from the selection set. The accuracy, F1, true skill and Area Under the Curve (AUC) were marginally improved when the meteorological data was added to the a parallel random forest algorithm (parRF). When the model is applied to new data, the estimated accuracy of the prediction is higher in HG-8M, indicating that inclusion of relevant, local meteorological datasets improves the result.https://www.mdpi.com/2072-4292/14/7/1656flood susceptibilitymachine learningmeteorological dataimportant factorsrandom forest |
| spellingShingle | Heather McGrath Piper Nora Gohl Accessing the Impact of Meteorological Variables on Machine Learning Flood Susceptibility Mapping Remote Sensing flood susceptibility machine learning meteorological data important factors random forest |
| title | Accessing the Impact of Meteorological Variables on Machine Learning Flood Susceptibility Mapping |
| title_full | Accessing the Impact of Meteorological Variables on Machine Learning Flood Susceptibility Mapping |
| title_fullStr | Accessing the Impact of Meteorological Variables on Machine Learning Flood Susceptibility Mapping |
| title_full_unstemmed | Accessing the Impact of Meteorological Variables on Machine Learning Flood Susceptibility Mapping |
| title_short | Accessing the Impact of Meteorological Variables on Machine Learning Flood Susceptibility Mapping |
| title_sort | accessing the impact of meteorological variables on machine learning flood susceptibility mapping |
| topic | flood susceptibility machine learning meteorological data important factors random forest |
| url | https://www.mdpi.com/2072-4292/14/7/1656 |
| work_keys_str_mv | AT heathermcgrath accessingtheimpactofmeteorologicalvariablesonmachinelearningfloodsusceptibilitymapping AT pipernoragohl accessingtheimpactofmeteorologicalvariablesonmachinelearningfloodsusceptibilitymapping |