Comparison of FIS and ARIMA models in runoff estimating
The ability to surface runoff modelling plays an important role in the water resources management, and the possibility of estimating and predicting of runoff values takes on particular importance in the case of gaps in the recorded time series. Therefore, this study aims to compare between fuzzy inf...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/47/e3sconf_form2023_05014.pdf |
| _version_ | 1797745584588718080 |
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| author | Slieman Alaa Ali Kozlov Dmitry |
| author_facet | Slieman Alaa Ali Kozlov Dmitry |
| author_sort | Slieman Alaa Ali |
| collection | DOAJ |
| description | The ability to surface runoff modelling plays an important role in the water resources management, and the possibility of estimating and predicting of runoff values takes on particular importance in the case of gaps in the recorded time series. Therefore, this study aims to compare between fuzzy inference system (FIS) models and autoregressive integrated moving average (ARIMA) models in estimating of the surface runoff at Al-Jawadiyah hydrometric station on the Orontes River in Syria. The MATLAB program was used to build the fuzzy inference models and the Minitab program to build the ARIMA models. A large number of fuzzy inference models were built with the change in the model parameters such as the type and number of membership functions and training algorithms. Likewise, a large number of ARIMA models were built with the change in autoregressive components, moving average components, and differences. The effect of seasonality on the model was also studied. Several criteria were used to compare the models and choose the best model, such as correlation coefficient and root mean square errors. The results showed that fuzzy inference models are superior to estimating surface runoff values with high reliability compared with ARIMA models. This study recommends creating complete databases for all factors related to water resources in the study area that can be relied upon in future studies. |
| first_indexed | 2024-03-12T15:25:24Z |
| format | Article |
| id | doaj.art-17d679174eee4ab28974282a9837a1ed |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T15:25:24Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-17d679174eee4ab28974282a9837a1ed2023-08-10T13:16:31ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014100501410.1051/e3sconf/202341005014e3sconf_form2023_05014Comparison of FIS and ARIMA models in runoff estimatingSlieman Alaa Ali0Kozlov Dmitry1Moscow State University of Civil EngineeringMoscow State University of Civil EngineeringThe ability to surface runoff modelling plays an important role in the water resources management, and the possibility of estimating and predicting of runoff values takes on particular importance in the case of gaps in the recorded time series. Therefore, this study aims to compare between fuzzy inference system (FIS) models and autoregressive integrated moving average (ARIMA) models in estimating of the surface runoff at Al-Jawadiyah hydrometric station on the Orontes River in Syria. The MATLAB program was used to build the fuzzy inference models and the Minitab program to build the ARIMA models. A large number of fuzzy inference models were built with the change in the model parameters such as the type and number of membership functions and training algorithms. Likewise, a large number of ARIMA models were built with the change in autoregressive components, moving average components, and differences. The effect of seasonality on the model was also studied. Several criteria were used to compare the models and choose the best model, such as correlation coefficient and root mean square errors. The results showed that fuzzy inference models are superior to estimating surface runoff values with high reliability compared with ARIMA models. This study recommends creating complete databases for all factors related to water resources in the study area that can be relied upon in future studies.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/47/e3sconf_form2023_05014.pdfsurface runofffuzzy inference systemarimamodelling estimationpredicting |
| spellingShingle | Slieman Alaa Ali Kozlov Dmitry Comparison of FIS and ARIMA models in runoff estimating E3S Web of Conferences surface runoff fuzzy inference system arima modelling estimation predicting |
| title | Comparison of FIS and ARIMA models in runoff estimating |
| title_full | Comparison of FIS and ARIMA models in runoff estimating |
| title_fullStr | Comparison of FIS and ARIMA models in runoff estimating |
| title_full_unstemmed | Comparison of FIS and ARIMA models in runoff estimating |
| title_short | Comparison of FIS and ARIMA models in runoff estimating |
| title_sort | comparison of fis and arima models in runoff estimating |
| topic | surface runoff fuzzy inference system arima modelling estimation predicting |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/47/e3sconf_form2023_05014.pdf |
| work_keys_str_mv | AT sliemanalaaali comparisonoffisandarimamodelsinrunoffestimating AT kozlovdmitry comparisonoffisandarimamodelsinrunoffestimating |