Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models
ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients be...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Baghdad
2023-05-01
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| Series: | Journal of Engineering |
| Subjects: | |
| Online Access: | https://joe.uobaghdad.edu.iq/index.php/main/article/view/2034 |
| _version_ | 1797782891037458432 |
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| author | Rafa H. AL-Suhaili Rizgar A. Karim |
| author_facet | Rafa H. AL-Suhaili Rizgar A. Karim |
| author_sort | Rafa H. AL-Suhaili |
| collection | DOAJ |
| description |
ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data set sub-division into training, testing and holdout data sub-sets, and different number of hidden nodes in the hidden layer. It is found that it is not necessary that the nearest station to the station under prediction has the highest effect; this may be attributed to the high differences in elevation between the stations. It can also found that the variance is not necessary has effect on the correlation coefficient obtained.
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| first_indexed | 2024-03-13T00:18:21Z |
| format | Article |
| id | doaj.art-6db85dad8b75455993153ee338698687 |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-13T00:18:21Z |
| publishDate | 2023-05-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-6db85dad8b75455993153ee3386986872023-07-11T18:34:50ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392023-05-0120310.31026/j.eng.2014.03.02Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network ModelsRafa H. AL-Suhaili Rizgar A. Karim ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data set sub-division into training, testing and holdout data sub-sets, and different number of hidden nodes in the hidden layer. It is found that it is not necessary that the nearest station to the station under prediction has the highest effect; this may be attributed to the high differences in elevation between the stations. It can also found that the variance is not necessary has effect on the correlation coefficient obtained. https://joe.uobaghdad.edu.iq/index.php/main/article/view/2034ANN models, monthly precipitation data, weather station networks, prediction, spatial distribution of precipitation. |
| spellingShingle | Rafa H. AL-Suhaili Rizgar A. Karim Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models Journal of Engineering ANN models, monthly precipitation data, weather station networks, prediction, spatial distribution of precipitation. |
| title | Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models |
| title_full | Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models |
| title_fullStr | Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models |
| title_full_unstemmed | Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models |
| title_short | Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models |
| title_sort | spatial prediction of monthly precipitation in sulaimani governorate using artificial neural network models |
| topic | ANN models, monthly precipitation data, weather station networks, prediction, spatial distribution of precipitation. |
| url | https://joe.uobaghdad.edu.iq/index.php/main/article/view/2034 |
| work_keys_str_mv | AT rafahalsuhaili spatialpredictionofmonthlyprecipitationinsulaimanigovernorateusingartificialneuralnetworkmodels AT rizgarakarim spatialpredictionofmonthlyprecipitationinsulaimanigovernorateusingartificialneuralnetworkmodels |