Abstract
In recent years, the soft computing techniques have arisen as an alternative for overcoming the limitations of traditional methods. Artificial neural networks (ANNs) can effectively approximate the nonlinear relationship between input and target parameters. Multiple linear regression (MLR) is also used widely to find the relationship between dependent and independent parameters. In the present study, the monthly inflows are predicted to compare the performance and reliability of ANN and MLR models for the Ukai reservoir. The performance measures have been computed to evaluate the model performance. The models based on ANNs with lesser values of RMSE and higher values of co-efficient of determination proved to be more accurate. The results demonstrate that ANN is reliable and effective tool as compared to multiple linear regression (MLR) and can be adopt as a better alternative to make predictions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nguyen TT, Baxter H, Barber ME, Hossain A, Orr CH, Adam JC (2013) Impacts of future changes on groundwater recharge and flow in highly-connected river-aquifer systems: A case study of the Spokane Valley-Rathdrum Prairie Aquifer. In AGU Fall Meeting Abstracts (Vol. 2013, pp. H23P–07)
Awchi TA, Srivastava DK (2009) Analysis of drought and storage for mula project using ANN and stochastic generation models. Hydrol Res 40(1):79–91
Aichouri I, Hani A, Bougherira N, Djabri L, Chaffai H, Lallahem S (2015) River flow model using artificial neural networks. Energy Proc 74:1007–1014
Hamzah FB, Hamzah FM, Razali SFM, Samad H (2021) A comparison of multiple imputation methods for recovering missing data in hydrological studies. Civil Eng J 7(9):1608–1619
Ghourdoyee Milan S, Aryaazar N, Javadi S, Razdar B (2020) Simulation of groundwater head using LS-SVM and comparison with ANN & MLR. Hydrogeology 5(1):118–133
Turhan E (2021) A comparative evaluation of the use of artificial neural networks for modeling the rainfall-runoff relationship in water resources management. J Ecol Eng 22(5):166–178
Mohammadi B, Moazenzadeh R, Christian K, Duan Z (2021) Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models. Environ Sci Pollut Res 1–17
Patle GT, Chettri M, Jhajharia D (2020) Monthly pan evaporation modelling using multiple linear regression and artificial neural network techniques. Water Supply 20(3):800–808
Gaya MS, Abba SI, Abdu AM, Tukur AI, Saleh MA, Esmaili P, Wahab NA (2020) Estimation of water quality index using artificial intelligence approaches and multi-linear regression. Int J Artif Intell 2252:8938
Poul AK, Shourian M, Ebrahimi H (2019) A comparative study of MLR, KNN, ANN and ANFIS models with wavelet transform in monthly stream flow prediction. Water Resour Manage 33(8):2907–2923
Noor CWM, Mamat R, Ahmed AN (2018) Comparative study of artificial neural network and mathematical model on marine diesel engine performance prediction. Int J Innov Comput Inform Control 14(3):959–969
Nathan NS, Saravanane R, Sundararajan T (2017) Application of ANN and MLR models on groundwater quality using CWQI at lawspet, Puducherry in India. J Geosci Environ Protect 5(03):99
Singh VK, Kumar P, Singh BP (2016) Rainfall-runoff modeling using artificial neural networks (ANNs) and multiple linear regression (MLR) techniques. Indian J Ecol 43(2):436–442
Mustafa MR, Isa MH, Rezaur RB (2012) Artificial neural networks modeling in water resources engineering: infrastructure and applications. In: Proceedings of World academy of science, engineering and technology (No. 62), February, World Academy of Science, Engineering and Technology
Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ Model Softw 22(1):2–13
Melesse AM, Ahmad S, McClain ME, Wang X, Lim YH (2011) Suspended sediment load prediction of river systems: An artificial neural network approach. Agric Water Manage 98(5):855–866
Ghorbani MA, Hosseini SH, Fazelifard MH, Abbasi H (2013) Sediment load estimation by MLR, ANN, NF and sediment rating curve (SRC) in Rio Chama river. J Civil Eng Urbanism 3(4):136–141
Raman H, Chandramouli V (1996) Deriving a general operating policy for reservoirs using neural network. J water resour plann manage 122(5):342–347
Jain SK, Das A, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J water resour plann manage 125(5):263–271
Kang KW, Kim JH, Park CY, Ham KJ (1993) Evaluation of hydrologic forecasting system based on neural network model. In: proceedings of the congress-international association for hydraulic research,1:(257–257). local organizing committee of the xxv congress
Adamowski J, Fung Chan H, Prasher SO, Ozga‐Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48(1)
Adamowski JF (2008) Peak daily water demand forecast modeling using artificial neural networks. J Water Resour Plan Manag 134(2):119–128
Riad S, Mania J, Bouchaou L, Najjar Y (2004) Rainfall-runoff model using an artificial neural network approach. Math Comput Model 40(7–8):839–846
Sahay RR, Sehgal V (2013) Wavelet regression models for predicting flood stages in rivers: a case study in Eastern India. J Flood Risk Manage 6(2):146–155
Rezaeianzadeh M, Tabari H, Yazdi AA, Isik S, Kalin L (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput Appl 25(1):25–37
Magar RB, Jothiprakash V (2011) Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models. J earth syst science 120:1067–1084
Tabari H, Sabziparvar AA, Ahmadi M (2011) Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorol Atmos Phys 110:135–142
Awchi TA (2014) River discharges forecasting in northern Iraq using different ANN techniques. Water Resour Manage 28(3):801–814
Cigizoglu HK (2008) Artificial neural networks in water resources. In: Integration of information for environmental security, Springer, Dordrecht, pp 115–148
Sehgal V, Tiwari MK, Chatterjee C (2014) Wavelet bootstrap multiple linear regression-based hybrid modeling for daily river discharge forecasting. Water Resour Manage 28(10):2793–2811
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Panchal, A., Yadav, S.M. (2023). Comparison of Multiple Linear Regression and Artificial Neural Network for Inflow Prediction of Ukai Reservoir. In: Timbadiya, P.V., Patel, P.L., Singh, V.P., Mirajkar, A.B. (eds) Geospatial and Soft Computing Techniques. HYDRO 2021. Lecture Notes in Civil Engineering, vol 339. Springer, Singapore. https://doi.org/10.1007/978-981-99-1901-7_34
Download citation
DOI: https://doi.org/10.1007/978-981-99-1901-7_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1900-0
Online ISBN: 978-981-99-1901-7
eBook Packages: EngineeringEngineering (R0)