Abstract
Due to the rapid growth in population, industrialization and agricultural outputs, the stress on groundwater and surface water has increased exponentially. The water quality depends on the interaction of both the groundwater and surface water; therefore, management and monitoring of the surface water are the need of the hour. River water management is a major environmental challenge worldwide. Because of the nonlinear behaviour of various water quality parameters, estimating the water quality of a surface water at any point of its flow is a time-consuming task. River quality monitoring is a difficult, cumbersome, and costly process that can lead to many analytical errors. Therefore, the main objective of this work is to create a reliable model for assessing and forecasting changes in water quality in Prayagraj (earlier know as Allahabad), Uttar Pradesh, India at three separate places, including the Ganga River, Yamuna River, and confluence of both rivers (also known as Sangam) using artifical neural network (ANN) and genetic algorithm (GA) models. The developed model was used to statistically compare the results by analysing samples collected from the selected stations fortnightly. Based on the correlation matrix of the water quality for three stations, general prediction models for the selected parameters, namely DO, hardness, turbidity, and BOD, were developed. The prediction model was developed for DO, hardness, and turbidity for station 1 (Ganga River). The results showed that the correlation coefficient (R) for the ANN prediction model is 0.97, the average absolute relative error (AARE) is 0.002, and the model efficiency (ME) is 0.95 for the hardness prediction model. Similarly, the BOD-ANN prediction model performed well at station 2 (Yamuna River) and station 3 (Sangam), with R = 0.99, AARE = 0.006, root mean square error (RMSE) = 0.06, and ME = 0.99 at station 2. Overall, ANN outperforms all other modelling techniques for all four prediction models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Broadhurst A, Cipolla R (1999) The applications of uncalibrated occlusion junctions. In: BMVC, pp 1–10
Chakraborty K, Mehrotra K, Mohan CK, Ranka S (1992) Forecasting the behavior of multivariate time series using neural networks. Neural Netw 5(6):961–970
El-Shafie A, Noureldin AE, Taha MR, Basri H (2008) Neural network model for Nile River inflow forecasting based on correlation analysis of historical inflow data
Grubert JP (2003) Acid deposition in the eastern United States and neural network predictions for the future. J Environ Eng Sci 2(2):99–109
Lachtermacher G, Fuller JD (1994) Backpropagation in hydrological time series forecasting. In: Stochastic and statistical methods in hydrology and environmental engineering. Springer, Dordrecht, pp 229–242
Li S, Wunsch DC, O’Hair E, Giesselmann MG (2001) Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation. J Sol Energ Eng 123(4):327–332
Liong SY, Sivapragasam C (2002) Flood stage forecasting with support vector machines 1. J Am Water Resour Assoc 38(1):173–186
Lubna H, Masoom MR (2015) Hydro-dissection—a simple solution in difficult laparoscopic cholecystectomy. Mymensingh Med J MMJ 24(3):592–595
Makarynskyy O (2004) Improving wave predictions with artificial neural networks. Ocean Eng 31(5–6):709–724
Muttil N, Chau KW (2006) Neural network and genetic programming for modelling coastal algal blooms. Int J Environ Pollut 28(3/4):223
Schizas CN, Pattichis CS, Michaelides SC (1994) Forecasting, minimum temperature vvith short time-length data using. Neural Netw World 2(94):219–230
Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114
Wen CG, Lee CS (1998) A neural network approach to multiobjective optimization for water quality management in a river basin. Water Resour Res 34(3):427–436
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 Switzerland AG
About this chapter
Cite this chapter
Shukla, A.K., Singh, R., Singh, R.M., Singh, R.P. (2023). Water Quality Modelling and Parameter Assessment Using Machine Learning Algorithms: A Case Study of Ganga and Yamuna Rivers in Prayagraj, Uttar Pradesh, India. In: Shukla, P., Singh, P., Singh, R.M. (eds) Environmental Processes and Management. Water Science and Technology Library, vol 120. Springer, Cham. https://doi.org/10.1007/978-3-031-20208-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-20208-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20207-0
Online ISBN: 978-3-031-20208-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)