Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model
For estimation of maximum daily fresh snow accumulation (MDFSA), a novel model based on an artificial neural network (ANN) was proposed. Daily precipitation, mean temperature, and minimum temperature were used as the input data for the ANN model. The ANN model was regularized and trained using a set...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2019/2709351 |
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| author | Gun Lee Dongkyun Kim Hyun-Han Kwon Eunsoo Choi |
| author_facet | Gun Lee Dongkyun Kim Hyun-Han Kwon Eunsoo Choi |
| author_sort | Gun Lee |
| collection | DOAJ |
| description | For estimation of maximum daily fresh snow accumulation (MDFSA), a novel model based on an artificial neural network (ANN) was proposed. Daily precipitation, mean temperature, and minimum temperature were used as the input data for the ANN model. The ANN model was regularized and trained using a set of 19,923 data points, observed daily in South Korea between 1960 and 2016. Leave-one-out cross validation was performed to validate the model. When the input data were known at the gauged locations, the correlation coefficient between the observed MDFSA and the estimated one by the ANN model was 0.90. When the input data were spatially interpolated at ungauged locations using the ordinary kriging (OK) method, the correlation coefficient was 0.40. The difference in correlation coefficients between the two methods implies that, while the ANN model itself has good performance, a significant portion of the uncertainty of the estimated MDFSA at ungauged locations comes from high spatial variability of the input variables that cannot be captured by the network of in situ gauges. However, these correlation coefficients were significantly greater than the correlation coefficient obtained by spatially interpolating the MDFSA values with the OK method (R = 0.20). These findings suggest that our ANN model significantly reduces the uncertainty of the estimated MDFSA caused by its high spatial variability. |
| format | Article |
| id | doaj-art-22bec384f4e043aea88369acdc302af3 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-22bec384f4e043aea88369acdc302af32025-02-03T05:52:09ZengWileyAdvances in Meteorology1687-93091687-93172019-01-01201910.1155/2019/27093512709351Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network ModelGun Lee0Dongkyun Kim1Hyun-Han Kwon2Eunsoo Choi3Graduate Research Assistant, Department of Civil Engineering, Hongik University, Seoul, Republic of KoreaAssociate Professor, Department of Civil Engineering, Hongik University, Seoul, Republic of KoreaProfessor, Department of Civil Engineering, Chonbuk National University, Jeonju-si, Jeollabuk-do, Republic of KoreaProfessor, Department of Civil Engineering, Hongik University, Seoul, Republic of KoreaFor estimation of maximum daily fresh snow accumulation (MDFSA), a novel model based on an artificial neural network (ANN) was proposed. Daily precipitation, mean temperature, and minimum temperature were used as the input data for the ANN model. The ANN model was regularized and trained using a set of 19,923 data points, observed daily in South Korea between 1960 and 2016. Leave-one-out cross validation was performed to validate the model. When the input data were known at the gauged locations, the correlation coefficient between the observed MDFSA and the estimated one by the ANN model was 0.90. When the input data were spatially interpolated at ungauged locations using the ordinary kriging (OK) method, the correlation coefficient was 0.40. The difference in correlation coefficients between the two methods implies that, while the ANN model itself has good performance, a significant portion of the uncertainty of the estimated MDFSA at ungauged locations comes from high spatial variability of the input variables that cannot be captured by the network of in situ gauges. However, these correlation coefficients were significantly greater than the correlation coefficient obtained by spatially interpolating the MDFSA values with the OK method (R = 0.20). These findings suggest that our ANN model significantly reduces the uncertainty of the estimated MDFSA caused by its high spatial variability.http://dx.doi.org/10.1155/2019/2709351 |
| spellingShingle | Gun Lee Dongkyun Kim Hyun-Han Kwon Eunsoo Choi Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model Advances in Meteorology |
| title | Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model |
| title_full | Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model |
| title_fullStr | Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model |
| title_full_unstemmed | Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model |
| title_short | Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model |
| title_sort | estimation of maximum daily fresh snow accumulation using an artificial neural network model |
| url | http://dx.doi.org/10.1155/2019/2709351 |
| work_keys_str_mv | AT gunlee estimationofmaximumdailyfreshsnowaccumulationusinganartificialneuralnetworkmodel AT dongkyunkim estimationofmaximumdailyfreshsnowaccumulationusinganartificialneuralnetworkmodel AT hyunhankwon estimationofmaximumdailyfreshsnowaccumulationusinganartificialneuralnetworkmodel AT eunsoochoi estimationofmaximumdailyfreshsnowaccumulationusinganartificialneuralnetworkmodel |