Mean sea level modelling using the neural network along the Chennai coast
This study focuses on the trend analysis of sea level data along the Chennai coast and thereby checks the structural change in the dataset using the Chow method. This study also proposed a methodology for predicting the mean sea level with the feed-forward neural network (FFNN) and wavelet transform...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-01-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/14/1/66 |
| _version_ | 1797202838970957824 |
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| author | Adwait Thendiyath Roshni |
| author_facet | Adwait Thendiyath Roshni |
| author_sort | Adwait |
| collection | DOAJ |
| description | This study focuses on the trend analysis of sea level data along the Chennai coast and thereby checks the structural change in the dataset using the Chow method. This study also proposed a methodology for predicting the mean sea level with the feed-forward neural network (FFNN) and wavelet transform neural network (WTNN) models. The data analysis shows that a breakpoint is observed in the year 1994 and found an overall increasing trend during the selected time period at the Chennai coast. For model development, a better understanding of the influencing parameters of the sea level is essential. Hence, correlation analyses have been performed and found that wind speed, sea surface salinity, and surface pressure are influencing variables for modelling sea level data. Apparently, these influencing variables have been considered as potential inputs for model development. To compare the performance of all the developed models, the Root Mean Square Error, Correlation Coefficient, and Nash–Sutcliffe Efficiency (NSE) were utilized. The results of performance indices and the graphical indicators also show that WTNN Model 4 outperformed all the other developed models. It was noticed that the percentage increase in the efficiency of NSE was 29.52% for WTNN Model 4 as compared to other developed models.
HIGHLIGHTS
Detection of the breakpoint using the Chow method.;
Identification of major climatic variables affecting the sea level rise.;
Proposed a methodology for prediction of the sea level using climatic variables employing feed-forward neural network (FFNN) and wavelet transform neural network (WTNN).; |
| first_indexed | 2024-04-10T09:33:32Z |
| format | Article |
| id | doaj.art-cc2cb680be574639a6503a977fd934f0 |
| institution | Directory Open Access Journal |
| issn | 2040-2244 2408-9354 |
| language | English |
| last_indexed | 2024-04-24T08:09:48Z |
| publishDate | 2023-01-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Water and Climate Change |
| spelling | doaj.art-cc2cb680be574639a6503a977fd934f02024-04-17T08:12:07ZengIWA PublishingJournal of Water and Climate Change2040-22442408-93542023-01-01141668210.2166/wcc.2022.187187Mean sea level modelling using the neural network along the Chennai coastAdwaitThendiyath Roshni0 Department of Civil Engineering, National Institute of Technology Patna, Patna 800005, Bihar, India This study focuses on the trend analysis of sea level data along the Chennai coast and thereby checks the structural change in the dataset using the Chow method. This study also proposed a methodology for predicting the mean sea level with the feed-forward neural network (FFNN) and wavelet transform neural network (WTNN) models. The data analysis shows that a breakpoint is observed in the year 1994 and found an overall increasing trend during the selected time period at the Chennai coast. For model development, a better understanding of the influencing parameters of the sea level is essential. Hence, correlation analyses have been performed and found that wind speed, sea surface salinity, and surface pressure are influencing variables for modelling sea level data. Apparently, these influencing variables have been considered as potential inputs for model development. To compare the performance of all the developed models, the Root Mean Square Error, Correlation Coefficient, and Nash–Sutcliffe Efficiency (NSE) were utilized. The results of performance indices and the graphical indicators also show that WTNN Model 4 outperformed all the other developed models. It was noticed that the percentage increase in the efficiency of NSE was 29.52% for WTNN Model 4 as compared to other developed models. HIGHLIGHTS Detection of the breakpoint using the Chow method.; Identification of major climatic variables affecting the sea level rise.; Proposed a methodology for prediction of the sea level using climatic variables employing feed-forward neural network (FFNN) and wavelet transform neural network (WTNN).;http://jwcc.iwaponline.com/content/14/1/66artificial neural networkbreakpointchow methoddecomposition level sea level risewavelet neural network |
| spellingShingle | Adwait Thendiyath Roshni Mean sea level modelling using the neural network along the Chennai coast Journal of Water and Climate Change artificial neural network breakpoint chow method decomposition level sea level rise wavelet neural network |
| title | Mean sea level modelling using the neural network along the Chennai coast |
| title_full | Mean sea level modelling using the neural network along the Chennai coast |
| title_fullStr | Mean sea level modelling using the neural network along the Chennai coast |
| title_full_unstemmed | Mean sea level modelling using the neural network along the Chennai coast |
| title_short | Mean sea level modelling using the neural network along the Chennai coast |
| title_sort | mean sea level modelling using the neural network along the chennai coast |
| topic | artificial neural network breakpoint chow method decomposition level sea level rise wavelet neural network |
| url | http://jwcc.iwaponline.com/content/14/1/66 |
| work_keys_str_mv | AT adwait meansealevelmodellingusingtheneuralnetworkalongthechennaicoast AT thendiyathroshni meansealevelmodellingusingtheneuralnetworkalongthechennaicoast |