Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and Dam

Stephen Olushola Oladosu; Alfred Sunday Alademomi; James Bolarinwa Olaleye; Joseph Olalekan Olusina; Tosin Julius Salami

doi:10.46481/jnsps.2023.1028

Authors

Stephen Olushola Oladosu
olushola.oladosu@uniben.edu
Department of Geomatics, Faculty of Environmental Sciences, University of Benin, P.M.B. 1154, Edo State, Nigeria
Alfred Sunday Alademomi Department 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, Nigeria
James Bolarinwa Olaleye Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, P.M.B. 12003, Akoka, Lagos State, Nigeria
Joseph Olalekan Olusina Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, P.M.B. 12003, Akoka, Lagos State, Nigeria
Tosin Julius Salami Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, P.M.B. 12003, Akoka, Lagos State, Nigeria

Keywords:

ANFIS, Gully Erosion, Ikpoba Dam, Sedimentation

Abstract

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

Dimensions

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