Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and Dam
The study proposed an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) model capable of predicting sediment deposited in a dam and sediment loss-in-transit (SLIT) using the potential of a formulated mathematical relation. The input parameters consist of five members viz: the rainfall, the slope, the...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nigerian Society of Physical Sciences
2023-05-01
|
| Series: | Journal of Nigerian Society of Physical Sciences |
| Subjects: | |
| Online Access: | https://journal.nsps.org.ng/index.php/jnsps/article/view/1028 |
| _version_ | 1827943059312082944 |
|---|---|
| author | Stephen Olushola Oladosu Alfred Sunday Alademomi James Bolarinwa Olaleye Joseph Olalekan Olusina Tosin Julius Salami |
| author_facet | Stephen Olushola Oladosu Alfred Sunday Alademomi James Bolarinwa Olaleye Joseph Olalekan Olusina Tosin Julius Salami |
| author_sort | Stephen Olushola Oladosu |
| collection | DOAJ |
| description |
The study proposed an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) model capable of predicting sediment deposited in a dam and sediment loss-in-transit (SLIT) using the potential of a formulated mathematical relation. The input parameters consist of five members viz: the rainfall, the slope, the particle size, the velocity, and the computed total volume of sediment exited from two prominent gullies for 2017, 2018, and 2019. The outputs are the total volume of sediment deposited at the adjoining Ikpoba dam for 2017, 2018, and 2019, respectively. The Ordinary Least Square (OLS) regression model on sediment volume retained all covariates with p<0.05, explaining 93.8% of the variability in the dataset. The multicollinearity effect on the dataset was assessed using the Variance Inflation Factor (VIF) which was found not to pose a problem for (VIF<5). The model was validated using the (MSE), the (MAE), and the correlation coefficient (r). The best prediction was obtained as: (RMSE = 0.0423; R2 = 0.947). The predicted volume of sediment was 842,895.8547m3 with an error of -0.3295344% and the predicted volume of SLIT was 57,787.98m3 which is an indication that ANFIS performs satisfactorily in predicting sediment volume for the gullies and the dam respectively
|
| first_indexed | 2024-03-13T10:09:44Z |
| format | Article |
| id | doaj.art-a9cc268223a3422c9c7b00590180f476 |
| institution | Directory Open Access Journal |
| issn | 2714-2817 2714-4704 |
| language | English |
| last_indexed | 2024-03-13T10:09:44Z |
| publishDate | 2023-05-01 |
| publisher | Nigerian Society of Physical Sciences |
| record_format | Article |
| series | Journal of Nigerian Society of Physical Sciences |
| spelling | doaj.art-a9cc268223a3422c9c7b00590180f4762023-05-22T06:01:16ZengNigerian Society of Physical SciencesJournal of Nigerian Society of Physical Sciences2714-28172714-47042023-05-015210.46481/jnsps.2023.1028Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and DamStephen Olushola Oladosu0Alfred Sunday Alademomi 1James Bolarinwa Olaleye2Joseph Olalekan Olusina3Tosin Julius Salami4Department of Geomatics, Faculty of Environmental Sciences, University of Benin, P.M.B. 1154, Edo State, NigeriaDepartment of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, P.M.B. 12003, Akoka, Lagos State, Nigeria; Centre for Multidisciplinary Research and Innovation, Suite C59, New Bannex Plaza, Wuze 2, Abuja, NigeriaDepartment of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, P.M.B. 12003, Akoka, Lagos State, NigeriaDepartment of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, P.M.B. 12003, Akoka, Lagos State, NigeriaDepartment of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, P.M.B. 12003, Akoka, Lagos State, Nigeria The study proposed an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) model capable of predicting sediment deposited in a dam and sediment loss-in-transit (SLIT) using the potential of a formulated mathematical relation. The input parameters consist of five members viz: the rainfall, the slope, the particle size, the velocity, and the computed total volume of sediment exited from two prominent gullies for 2017, 2018, and 2019. The outputs are the total volume of sediment deposited at the adjoining Ikpoba dam for 2017, 2018, and 2019, respectively. The Ordinary Least Square (OLS) regression model on sediment volume retained all covariates with p<0.05, explaining 93.8% of the variability in the dataset. The multicollinearity effect on the dataset was assessed using the Variance Inflation Factor (VIF) which was found not to pose a problem for (VIF<5). The model was validated using the (MSE), the (MAE), and the correlation coefficient (r). The best prediction was obtained as: (RMSE = 0.0423; R2 = 0.947). The predicted volume of sediment was 842,895.8547m3 with an error of -0.3295344% and the predicted volume of SLIT was 57,787.98m3 which is an indication that ANFIS performs satisfactorily in predicting sediment volume for the gullies and the dam respectively https://journal.nsps.org.ng/index.php/jnsps/article/view/1028ANFIS, Gully Erosion, Ikpoba Dam, Sedimentation |
| spellingShingle | Stephen Olushola Oladosu Alfred Sunday Alademomi James Bolarinwa Olaleye Joseph Olalekan Olusina Tosin Julius Salami Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and Dam Journal of Nigerian Society of Physical Sciences ANFIS, Gully Erosion, Ikpoba Dam, Sedimentation |
| title | Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and Dam |
| title_full | Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and Dam |
| title_fullStr | Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and Dam |
| title_full_unstemmed | Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and Dam |
| title_short | Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and Dam |
| title_sort | evaluation of anfis predictive ability using computed sediment from gullies and dam |
| topic | ANFIS, Gully Erosion, Ikpoba Dam, Sedimentation |
| url | https://journal.nsps.org.ng/index.php/jnsps/article/view/1028 |
| work_keys_str_mv | AT stephenolusholaoladosu evaluationofanfispredictiveabilityusingcomputedsedimentfromgulliesanddam AT alfredsundayalademomi evaluationofanfispredictiveabilityusingcomputedsedimentfromgulliesanddam AT jamesbolarinwaolaleye evaluationofanfispredictiveabilityusingcomputedsedimentfromgulliesanddam AT josepholalekanolusina evaluationofanfispredictiveabilityusingcomputedsedimentfromgulliesanddam AT tosinjuliussalami evaluationofanfispredictiveabilityusingcomputedsedimentfromgulliesanddam |