Evaluation of Fuzzy Clustering and Artificial Neural Network Methods in Spatial Zoning of Annual Precipitation in Iran
Precipitation is one of the main elements of the Earth's hydro-climatic cycle and its variability depends on the complex and non-linear relationships between the climate system and environmental factors. Understanding these relationships and doing environmental planning based on them is difficu...
| Main Authors: | , |
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| Format: | Article |
| Language: | fas |
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Isfahan University of Technology
2023-05-01
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| Series: | علوم آب و خاک |
| Subjects: | |
| Online Access: | http://jstnar.iut.ac.ir/article-1-4249-en.pdf |
| _version_ | 1797784161615872000 |
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| author | A. Shahbaee Kotenaee H. Asakereh |
| author_facet | A. Shahbaee Kotenaee H. Asakereh |
| author_sort | A. Shahbaee Kotenaee |
| collection | DOAJ |
| description | Precipitation is one of the main elements of the Earth's hydro-climatic cycle and its variability depends on the complex and non-linear relationships between the climate system and environmental factors. Understanding these relationships and doing environmental planning based on them is difficult. Therefore, classifying data and dividing information into homogeneous and small categories can be helpful in this regard. In the present study, an attempt was made to prepare precipitation, altitude, slope, slope direction, and station density data for 3423 synoptic, climatological, and gauge stations in Iran in the 1961-2015 years’ period. These data were entered into fuzzy (FCM), self-organizing map neural network (SOM-ANN) models and precipitation-spatial zoning. The outputs of the two models were compared in terms of accuracy and efficiency. The results obtained from the output of the models have divided the rainfall conditions of Iran into four zones concerning environmental factors. Evaluations also showed that both models had high accuracy in classifying precipitation parameters; However, the fuzzy model has a relative advantage over the neural network model in the accuracy of results. |
| first_indexed | 2024-03-13T00:36:01Z |
| format | Article |
| id | doaj.art-a801f9c576b74b99a5927ad947e3a02d |
| institution | Directory Open Access Journal |
| issn | 2476-3594 2476-5554 |
| language | fas |
| last_indexed | 2024-03-13T00:36:01Z |
| publishDate | 2023-05-01 |
| publisher | Isfahan University of Technology |
| record_format | Article |
| series | علوم آب و خاک |
| spelling | doaj.art-a801f9c576b74b99a5927ad947e3a02d2023-07-10T05:57:38ZfasIsfahan University of Technologyعلوم آب و خاک2476-35942476-55542023-05-012711732Evaluation of Fuzzy Clustering and Artificial Neural Network Methods in Spatial Zoning of Annual Precipitation in IranA. Shahbaee Kotenaee0H. Asakereh1 Zanjan University Zanjan University Precipitation is one of the main elements of the Earth's hydro-climatic cycle and its variability depends on the complex and non-linear relationships between the climate system and environmental factors. Understanding these relationships and doing environmental planning based on them is difficult. Therefore, classifying data and dividing information into homogeneous and small categories can be helpful in this regard. In the present study, an attempt was made to prepare precipitation, altitude, slope, slope direction, and station density data for 3423 synoptic, climatological, and gauge stations in Iran in the 1961-2015 years’ period. These data were entered into fuzzy (FCM), self-organizing map neural network (SOM-ANN) models and precipitation-spatial zoning. The outputs of the two models were compared in terms of accuracy and efficiency. The results obtained from the output of the models have divided the rainfall conditions of Iran into four zones concerning environmental factors. Evaluations also showed that both models had high accuracy in classifying precipitation parameters; However, the fuzzy model has a relative advantage over the neural network model in the accuracy of results.http://jstnar.iut.ac.ir/article-1-4249-en.pdfself-organizing map neural networkfuzzy modelzoningprecipitationiran |
| spellingShingle | A. Shahbaee Kotenaee H. Asakereh Evaluation of Fuzzy Clustering and Artificial Neural Network Methods in Spatial Zoning of Annual Precipitation in Iran علوم آب و خاک self-organizing map neural network fuzzy model zoning precipitation iran |
| title | Evaluation of Fuzzy Clustering and Artificial Neural Network Methods in Spatial Zoning of Annual Precipitation in Iran |
| title_full | Evaluation of Fuzzy Clustering and Artificial Neural Network Methods in Spatial Zoning of Annual Precipitation in Iran |
| title_fullStr | Evaluation of Fuzzy Clustering and Artificial Neural Network Methods in Spatial Zoning of Annual Precipitation in Iran |
| title_full_unstemmed | Evaluation of Fuzzy Clustering and Artificial Neural Network Methods in Spatial Zoning of Annual Precipitation in Iran |
| title_short | Evaluation of Fuzzy Clustering and Artificial Neural Network Methods in Spatial Zoning of Annual Precipitation in Iran |
| title_sort | evaluation of fuzzy clustering and artificial neural network methods in spatial zoning of annual precipitation in iran |
| topic | self-organizing map neural network fuzzy model zoning precipitation iran |
| url | http://jstnar.iut.ac.ir/article-1-4249-en.pdf |
| work_keys_str_mv | AT ashahbaeekotenaee evaluationoffuzzyclusteringandartificialneuralnetworkmethodsinspatialzoningofannualprecipitationiniran AT hasakereh evaluationoffuzzyclusteringandartificialneuralnetworkmethodsinspatialzoningofannualprecipitationiniran |