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
[email protected]
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

REFERENCES

S. Chakraverty & S. Mall, “Artificial Neural Networks for Engineers and ScientistsSolving Ordinary Di erential Equations”, CRC Press 168 (2017) 80.

S. Akram & R. Hossien, “Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models”, Environmental Science and Pollution Research (2018).

V. Umarania, A. Juliana & J. Deepa, “Sentiment Analysis using various Machine Learning and Deep Learning Techniques”, J. Nig. Soc. Phys. Sci. 3 (2021) 308.

C. I. Udeze, I. E. Eteng & A. E. Ibor, “Application of Machine Learning and Resampling Techniques to Credit Card Fraud Detection” J. Nig. Soc. Phys. Sci. 4 (2022) 769.

B. N. Hikona, G. G. Yebpellaa, L. Jafiyab & S. Ayuba, “Preliminary Investigation of Microplastic as a Vector for HeavyMetals in Bye-ma Salt Mine, Wukari, Nigeria”, J. Nig. Soc. Phys. Sci. 3 (2021) 259.

G. O. Aigbadon, A. Ocheli, & E. O. Akudo, “Geotechnical evaluation of gully erosion and landslides materials and their impact in Iguosa and its environs, southern Nigeria”, Environ Syst Res 10 (2021) 36.

J. C. Egbueri, O. Igwe & C. O. Unigwe, “Gully slope distribution characteristics and stability analysis for soil erosion risk ranking in parts of southeastern Nigeria: a case study”, Environ Earth Sci 80 (2021) 292.

J. O. Ehiorobo & O. C. Izinyon, “Monitoring Gully Formation and Development for Effective Remediation and Control”, TS09I - Engineering Surveying, 5919. FIG Working Week 2012. Knowing to manage the territory, protect the environment, evaluate the cultural heritage Rome, Italy (2012) 6.

J. O. Ehiorobo, & O. C. Izinyon, “Monitoring of Soil Loss from Erosion Using Geoinformatics and Geotechnical Engineering Methods”. (2011). Retrieved June 10, 2021, from https://www.fig.net/resources/proceedings/2011.

O. Igwe, U. I. John, O. Solomon & O. Obinna., “GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria”, Geoenvironmental

Disasters 7 (2020) 32.

S. E. Okonofua & N. O. Uwadia, “Evaluating Factors Responsible for Gully Development at the University of Benin. Scholarlink Research Institute Journals”, Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 4 (2013) 5.

S. Dahiru, “Nigeria Erosion and Watershed Management Project”, (NEWMAP). (2020). Retrieved July 20, 2021, from https://newmap.gov.ng/index.php.

World Bank, “Nigeria Erosion and Watershed Management Project (NEWMAP) Additional Financing” (2017). https://documents1.worldbank.org/curated/en/193161525194153230. Deposited July 10, 2022.

N. Q. Sultan, E. Isa & R. M. Hossien, “Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms”, Journal of Applied Research in Water and Wastewater 4 (2017) 1.

X. Zhang, J. Fan, Q. Liu & D. Xiong, “The contribution of gully erosion to total sediment production in a small watershed in Southwest China”, Physical Geography 39 (2018) 3.

Y. Guan, S. Yang, C. Zhao, H. Lou, K. Chen, C. Zhang & B. Wu, “Monitoring long-term gully erosion and topographic thresholds in the marginal zone of the Chinese Loess Plateau”, Soil and Tillage Research 205 (2021) 104800.

I. Ionita, M. A. Fullen, W. Zg?obicki & J. Poesen, “Gully erosion as a natural and human-induced hazard”, Nat Hazards 79 (2015) 1.

C. Jiang, W. Fan, N. Yu & E. Liu, “Spatial modeling of gully head erosion on the Loess Plateau using a certainty factor and random forest model”, Science of the Total Environment 783 (2021).

I. Marzolff, J. Poeen & J. B. Ries, “Short to medium-term gully development: human activity and gully erosion variability in selected Spanish gully catchments”, Landform Anal 17 (2011) 111.

P. K. Shit, G. Bhunia & R. Maiti, “Morphology and development of selected Badlands in South Bengal (India)”, Indian Journal of Geography and Environment 13 (2014) 161.

G. Chen, “A simple way to deal with multicollinearity”, Journal of Applied Statistics 39 (2012) 9.

J. Poesen, J. Nachtergaele, G. Verstraeten & C. Valentin, “Gully erosion and environmental change: Importance and research needs”, Catena 50 (2003) 91.

A. Majhi, J. Nyssen & A. Verdoodt, “What is the best technique to estimate topographic thresholds of gully erosion? Insights from a case study on the permanent gullies of Rarh plain, India”, Geomorphology 375 (2021) 107547.

J. S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system”, IEEE Trans. Syst. Man Cybern. 23 (1993) 665.

J. S. R. Jang, “Input selection for ANFIS learning” In Proceedings of the Fifth IEEE International Conference on Fuzzy Systems (1996) 1493.

J. S. R. Jang & E. Mizutani, “Levenberg–Marquardt method for ANFIS learning”, In Fuzzy Information Processing Society, Biennial Conference of the North American (1996) 87.

D. K. Ghosea, S. S. Pandab & P. C. Swainc, “Prediction and optimization of runoff via ANFIS and GA”, Alexandria Engineering Journal 52 (2013) 2.

T. Gokmen, O. Serhan & P. S. Vijay, “Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces”, Advances in Water Resources 26 (2003) 1249.

A. B. Dariane & S. H. Azimi, “Forecasting streamflow by combination of genetic input selection algorithm and wavelet transform using ANFIS model”, Hydrolog. Sci. J. 61 (2016) 585.

A. Mustafa, “Evaluation of different types of artificial intelligence methods to model the suspended sediment load in Tigris River”, MATEC Web of Conferences 162 (2018) 2.

H. M. Azamathulla & A. A. Ghani, “ANFIS-based approach for predicting the scour depth at culvert outlets”, Journal of Pipeline Systems Engineering and Practice 2 (2011).

H. M. Azamathulla, A. A. Ghani & S. Y., Fei, “ANFIS-based approach for predicting sediment transport in clean sewer”, Applied soft computing 12 (2012) 3.

H. M. Azamathulla, K C. Chun, A. A. Ghani, A. Junaidah, N. A., Zakaria & Z. A. Hasan, “An ANFIS-based approach for predicting the bed load for moderately sized rivers”, Journal of Hydro-environment Research 3 (2009) 35.

C. I. Ikhile, “Geomorphology and Hydrology of the Benin Region, Edo State, Nigeria”, International Journal of Geosciences 7 (2016).

C. N. Akujieze, “Effects of Anthropogenic Activities (Sand Quarrying and Waste Disposal) on Urban Groundwater System and Aquifer Vulnerability Assessment in Benin City, Edo State, Nigeria”. PhD Thesis, University of Benin, Benin City, Nigeria (2004).

P. Hjorth & L. Bengtsson, “Large Dams, Statistics and Critical Review”. In: Bengtsson L., Herschy R.W., Fairbridge R.W. (eds) Encyclopedia of Lakes and Reservoirs, Encyclopedia of Earth Sciences Series, Springer, Dordrecht (2012).

C. N. Ezugwu, B. U. Anyata & E. O. Ekenta, “Estimation of The Life of Ikpoba River Reservoir”, International Journal of Engineering Research & Technology (IJERT) 2 (2013) 8.

Edo State Urban Water Board, Construction information of Ikpoba Dam Archive (2007).

S. O. Oladosu, L. M. Ojigi, V. E. Aturuocha, C. O. Anekwe & R. Tanko, “An investigative study on the volume of sediment accumulation in Tagwai dam reservoir using bathymetric and geostatistical analysis techniques”, SN Applied Sciences 1 (2019) 492.

International Hydrographic Organization (IHO) Standards for Hydrographic Surveys 6th Edition IHO Publication (2020) 44.

B. Selma, S. Chouraqui & H. Aboua¨?ssa, “Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task”, SN Appl. Sci. 2 (2020) 878.

M. Sugeno, T. Kang&G. Kang, “Structure Identification of Fuzzy Model. Fuzzy Sets and Systems”, 28 (1988) 9.

T. Takagi & M. Sugeno, “Fuzzy Identification of Systems and Its Application to Modeling and Control”, IEEE Transactions on Systems, Man, and Cybernetics, SMC-15 (1985).

A. Tarno, Rusgiyono & Sugito, “Adaptive Neuro Fuzzy Inference System (ANFIS) approach for modeling paddy production data in Central Java”, IOP Conf. Series: Journal of Physics: Conf. Series 1217 (2019) 012083.

N. A. Adnan, H. Maizah & R. A. Adnan, “Comparative study on some methods for handling multicollinearity problems”, Mathematika 22 (2006) 2.

M. Kutner, C. J. Nacctsheim & J. Neter, “Applied Linear Regression Model”. Technometrics 26 (2004) 4.

D. W. Marquardt, “Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation”, Technometrics 12 (1970) 59.

D. O’brien & P. S. Scott, “Correlation and Regression”, in Approaches to Quantitative Research-A guide for Dissertation Students, Ed, Chen, H, Oak Tree Press (2012).

R. M. O’brien, “A Caution Regarding Rule of Thumb for Variance Inflation Factor”, Qual Quant. 41 (2007) 673.