Skip to main content

Comparison of Multiple Linear Regression and Artificial Neural Network for Inflow Prediction of Ukai Reservoir

  • Conference paper
  • First Online:
Geospatial and Soft Computing Techniques (HYDRO 2021)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 339))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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)

    Google Scholar 

  2. 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

    Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Google Scholar 

  8. 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

    Article  Google Scholar 

  9. 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

    Google Scholar 

  10. 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

    Article  Google Scholar 

  11. 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

    Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Google Scholar 

  14. 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

    Google Scholar 

  15. 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

    Google Scholar 

  16. 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

    Google Scholar 

  17. 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

    Google Scholar 

  18. Raman H, Chandramouli V (1996) Deriving a general operating policy for reservoirs using neural network. J water resour plann manage 122(5):342–347

    Google Scholar 

  19. 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

    Google Scholar 

  20. 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

    Google Scholar 

  21. 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)

    Google Scholar 

  22. Adamowski JF (2008) Peak daily water demand forecast modeling using artificial neural networks. J Water Resour Plan Manag 134(2):119–128

    Article  Google Scholar 

  23. 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

    Article  MATH  Google Scholar 

  24. 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

    Google Scholar 

  25. 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

    Article  Google Scholar 

  26. 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

    Google Scholar 

  27. 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

    Google Scholar 

  28. Awchi TA (2014) River discharges forecasting in northern Iraq using different ANN techniques. Water Resour Manage 28(3):801–814

    Article  Google Scholar 

  29. Cigizoglu HK (2008) Artificial neural networks in water resources. In: Integration of information for environmental security, Springer, Dordrecht, pp 115–148

    Google Scholar 

  30. 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sanjaykumar M. Yadav .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics