Estimation of Total Suspended Solids Concentration in Streams Using Regression and Artificial Neural Networks Methods
In this study considering total suspended solids (TSS) parameter monitored in a stream watershed, the predictability of upstream values from downstream data was investigated using regression analysis, which were applied to linear, power, exponential, and quadratic functions, and artificial neural...
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Artvin Coruh University
2023-01-01
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| Series: | Doğal Afetler ve Çevre Dergisi |
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| Online Access: | http://dacd.artvin.edu.tr/tr/download/article-file/2499847 |
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| author | Betül Mete Sinan Nacar Adem Bayram Osman Tuğrul Bak |
| author_facet | Betül Mete Sinan Nacar Adem Bayram Osman Tuğrul Bak |
| author_sort | Betül Mete |
| collection | DOAJ |
| description | In this study considering total suspended solids (TSS) parameter monitored in a stream watershed, the predictability of upstream values
from downstream data was investigated using regression analysis, which were applied to linear, power, exponential, and quadratic
functions, and artificial neural networks (ANNs) method. The data were obtained within the scope of sampling studies carried out 40
times between June 2019 and March 2020 at eight monitoring stations selected in the Sera Stream Watershed (Trabzon). The
monitoring stations were divided into two groups as upstream, the first four, and downstream, the last four, stations. Half of
downstream data (two stations) was used for training, a quarter (one station) for validation, and the rest (one station) for testing. Two
models with different combinations of independent variables were established. In the first model (M1), only the TSS values, and in the
other model (M2), the month and week information of the sampling dates were digitized and used as independent variables, in addition
to the TSS values. Root mean square error, mean absolute error, and Nash-Sutcliffe (NS) efficiency coefficient statistics were used to
evaluate the model and method performances. Compared to other functions, the power one had the best estimation results in the
regression analysis. On the other hand, the ANNs method gave better results than the regression analysis. In both methods, M2
performed better overall. In the ANNs method, the NS efficiency coefficients obtained from M1 and M2 were calculated as 0.980 and
0.997, respectively, for the training, and 0.978 and 0.978, respectively, for the testing data sets. Considering the efficiency values, it
has been understood that the use of date information as an independent variable will positively affect the model performance in the
stream TSS modeling studies. Within the scope of this study, it has been concluded that upstream TSS values can be successfully
estimated from downstream TSS data in stream watersheds.
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| format | Article |
| id | doaj-art-ae6d0506f11f4682b0884061bf8689cd |
| institution | Kabale University |
| issn | 2528-9640 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Artvin Coruh University |
| record_format | Article |
| series | Doğal Afetler ve Çevre Dergisi |
| spelling | doaj-art-ae6d0506f11f4682b0884061bf8689cd2025-02-03T03:28:52ZengArtvin Coruh UniversityDoğal Afetler ve Çevre Dergisi2528-96402023-01-0191125135https://doi.org/10.21324/dacd.1133981Estimation of Total Suspended Solids Concentration in Streams Using Regression and Artificial Neural Networks MethodsBetül Mete0https://orcid.org/0000-0002-3689-6430Sinan Nacar1https://orcid.org/0000-0003-2497-5032Adem Bayram2https://orcid.org/0000-0003-4359-9183 Osman Tuğrul Bak3https://orcid.org/0000-0001-8694-0543Karadeniz Teknik Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü, 61080, TrabzonTokat Gaziosmanpaşa Üniversitesi, Mühendislik ve Mimarlık Fakültesi, İnşaat Mühendisliği Bölümü, 60150, Tokat.Karadeniz Teknik Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü, 61080, TrabzonKaradeniz Teknik Üniversitesi, Of Teknoloji Fakültesi, İnşaat Mühendisliği Bölümü, 61830, Trabzon.In this study considering total suspended solids (TSS) parameter monitored in a stream watershed, the predictability of upstream values from downstream data was investigated using regression analysis, which were applied to linear, power, exponential, and quadratic functions, and artificial neural networks (ANNs) method. The data were obtained within the scope of sampling studies carried out 40 times between June 2019 and March 2020 at eight monitoring stations selected in the Sera Stream Watershed (Trabzon). The monitoring stations were divided into two groups as upstream, the first four, and downstream, the last four, stations. Half of downstream data (two stations) was used for training, a quarter (one station) for validation, and the rest (one station) for testing. Two models with different combinations of independent variables were established. In the first model (M1), only the TSS values, and in the other model (M2), the month and week information of the sampling dates were digitized and used as independent variables, in addition to the TSS values. Root mean square error, mean absolute error, and Nash-Sutcliffe (NS) efficiency coefficient statistics were used to evaluate the model and method performances. Compared to other functions, the power one had the best estimation results in the regression analysis. On the other hand, the ANNs method gave better results than the regression analysis. In both methods, M2 performed better overall. In the ANNs method, the NS efficiency coefficients obtained from M1 and M2 were calculated as 0.980 and 0.997, respectively, for the training, and 0.978 and 0.978, respectively, for the testing data sets. Considering the efficiency values, it has been understood that the use of date information as an independent variable will positively affect the model performance in the stream TSS modeling studies. Within the scope of this study, it has been concluded that upstream TSS values can be successfully estimated from downstream TSS data in stream watersheds. http://dacd.artvin.edu.tr/tr/download/article-file/2499847total suspended solidsregression analysissera stream watershedartificial neural networks |
| spellingShingle | Betül Mete Sinan Nacar Adem Bayram Osman Tuğrul Bak Estimation of Total Suspended Solids Concentration in Streams Using Regression and Artificial Neural Networks Methods Doğal Afetler ve Çevre Dergisi total suspended solids regression analysis sera stream watershed artificial neural networks |
| title | Estimation of Total Suspended Solids Concentration in Streams Using Regression and Artificial Neural Networks Methods |
| title_full | Estimation of Total Suspended Solids Concentration in Streams Using Regression and Artificial Neural Networks Methods |
| title_fullStr | Estimation of Total Suspended Solids Concentration in Streams Using Regression and Artificial Neural Networks Methods |
| title_full_unstemmed | Estimation of Total Suspended Solids Concentration in Streams Using Regression and Artificial Neural Networks Methods |
| title_short | Estimation of Total Suspended Solids Concentration in Streams Using Regression and Artificial Neural Networks Methods |
| title_sort | estimation of total suspended solids concentration in streams using regression and artificial neural networks methods |
| topic | total suspended solids regression analysis sera stream watershed artificial neural networks |
| url | http://dacd.artvin.edu.tr/tr/download/article-file/2499847 |
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