Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments
In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2016/9125219 |
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| author | Ivana Sušanj Nevenka Ožanić Ivan Marović |
| author_facet | Ivana Sušanj Nevenka Ožanić Ivan Marović |
| author_sort | Ivana Sušanj |
| collection | DOAJ |
| description | In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error. |
| format | Article |
| id | doaj-art-6642190a840e4e1c881415ab0ef1e65a |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-6642190a840e4e1c881415ab0ef1e65a2025-02-03T05:59:07ZengWileyAdvances in Meteorology1687-93091687-93172016-01-01201610.1155/2016/91252199125219Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small CatchmentsIvana Sušanj0Nevenka Ožanić1Ivan Marović2Department of Hydrology and Geology, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, CroatiaDepartment of Hydrology and Geology, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, CroatiaDepartment of Construction Management, Technology & Architecture, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, CroatiaIn some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.http://dx.doi.org/10.1155/2016/9125219 |
| spellingShingle | Ivana Sušanj Nevenka Ožanić Ivan Marović Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments Advances in Meteorology |
| title | Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments |
| title_full | Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments |
| title_fullStr | Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments |
| title_full_unstemmed | Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments |
| title_short | Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments |
| title_sort | methodology for developing hydrological models based on an artificial neural network to establish an early warning system in small catchments |
| url | http://dx.doi.org/10.1155/2016/9125219 |
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