Abstract
Correlation stream gauge stations i.e. linking of discharge at upstream stations to find the discharge at the downstream station, is an important method which can be adopted. Corelating stations for discharge estimation plays a crucial role in the planning of hydrological applications, optimization of water resource allocations, pricing and water quality assessment, and agriculture and irrigation operations. Many data driven techniques have been seen to be utilized for this activity. The present study is an attempt to carry the baton forward with an aim of correlating the three stream gauging stations namely Ashti, Bhatpalli and Tekra which are situated in the Andhra Pradesh state at the Godavari River, India using Multi Gene Genetic Programming and Random Forest techniques. Previously measured streamflow values for the years of 1995–2013 at these three locations were used to develop the data driven models wherein stream flow at Tekra station is estimated using the stream flow values of the two upstream stations; Ashti and Bhatpalli. Monsoon monthly models and yearly models have been developed. All the models display better performance in estimating the stream flow at Tekra. The performance of developed models is judged by the traditional error measures along with the visual plots.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamowski JF (2008) Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J Hydrol 353:247–266
Breiman L (2001) Random forests. Mach Learn 45:5–32
Diez-Sierra J, del Jesus M (2019) Subdaily rainfall estimation through daily rainfall downscaling using Random Forests in Spain. Water 11:1–25
Granada F, Saroli M, de Marinis G, Gargano R (2018) Machine learning models for spring discharge forecasting. Geofluids. Article ID: 832816
James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 103 XIV. Springer, New York, Heidelberg, Dordrecht, London
Jain A, KumarJha S, Misra S (2008) Modeling and analysis of concrete slump using Artificial Neural Networks. J Mater Civ Eng 20:628–633
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA
Legates DR, McCabe GJ Jr (1991) Evaluating the use of “goodness of fit” measures in hydrological and hydro climatic model validation. Water Resour Res 35:233–241
Londhe SN (2008) Soft computing approach for real-time estimation of missing wave heights. Ocean Eng 35:1080–1089
Liu GR, Liu MB (2003) Smoothed Particle Hydrodynamics: a meshfree particle method. World Scientific Publishing, Singapore
Liang Z, Tang T, Li B, Liu T, Wang J, Hu Y (2018) Long-term streamflow forecasting using SWAT through the integration of the random forests precipitation generator: case study of Danjiangkou Reservoir. Hydrol Res 49:513–1523
Mosavi A, Ozturk P, Chau K (2018) Flood prediction using machine learning models: literature review. Water 10(11):1536,1–40.
Mengade A, Londhe SN, Dixit PR, Kulkarni P (2020) Correlating Stream Gauge stations using Multigene Genetic Programming. In: Proceedings of 25th international conference on hydraulics, water resources and coastal engineering (HYDRO 2020) held online, hosted by NIT Rourkela, India during 26–28 March 2021
Mehr AD, Nourani V (2017) Season algorithm-multigene genetic programming: a new approach for rainfall-runoff modelling. Water Resour Manage 32:2665–2679
Mehr AD, Kahya E (2017) A Pareto-optimal moving average Multigene Genetic Programming model for daily stream flow prediction. J Hydrol 549:603–615
Muñoz P, Orellana-Alvear J, Willems P, Célleri R (2018) Flash-flood forecasting in an Andean Mountain catchment—development of a step-wise methodology based on the Random Forest Algorithm. Water 10(11):15–19
Mohamed S, Ludovic O, Ribstein P (2019) Random forest ability in regionalizing hourly hydrological model parameters. Water 11(10):1540, 1–22
MCLeod S (2019) What does a box plot tell you. https://www.simplypsychology.org/boxplots.html
Pham LT, Lifeng L, Andrew OF (2021) Evaluation of Random Forest for short-term daily streamflow forecast in rainfall and snowmelt driven watersheds. Hydrol Earth Syst Sci 25:2997–3015
Prieto C, Vine NL, Kavetski D, Garcia E, Medina R (2019) Flow prediction in ungauged catchments using probabilistic Random Forests regionalization and new statistical adequacy tests. Water Resour Res 55:4364–4392
Pandey DS, Pan I, Das S, Leahy JJ, Kwapinski W (2015) Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier. Biores Technol 179:524–533
Pande CB et al (2022a) Forecasting of SPI and meteorological drought based on the artificial neural network and M5P model tree. Land 11, no 11:2040. https://doi.org/10.3390/land11112040
Pande CB (2022b) Land use/land cover and change detection mapping in rahuri watershed area (ms), india using the google earth engine and machine learning approach. Geocarto Int 37(22):1–21
Pande CB, Kadam SA, Jayaraman R, Gorantiwar S, Shinde M (2022c) Prediction of soil chemical properties using multispectral satellite images and wavelet transforms methods. J Saudi Soc Agric Sci 21:21–28
Pande CB, Costache R, Sammen SS et al (2023) Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India. Theor Appl Climatol 152:535–558. https://doi.org/10.1007/s00704-023-04426-z
Searson DP, Willis MJ, Montague GA (2007) Co-evolution of non-linear PLS model components. J Chemom 21(12):592–603
Searson DP (2015) GPTIPS 2: an open-source software platform for symbolic data mining. In: Chapter 22 in handbook of genetic programming applications, New York
Tongal H, Booij MJ (2018) Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. J Hydrol 564:266–282
Tzuc OM, Hernández-Pérez I, Macias-Melo EV, Bassam A, Xamán J, Cruz B (2019) Multi-gene genetic programming for predicting the heat gain of flat naturally ventilated roof using data from outdoor environmental monitoring. Measurement 138:106–117
William A, Burns A (1983) Mixed Station Extension of monthly stream flow records. J Hydraul Eng 109:10–11
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
Kulkarni, P., Dixit, P., Londhe, S. (2023). Coorelating Stream Guage Stations Using Multi Gene Genetic Programming and Random Forest. In: Pande, C.B., Kumar, M., Kushwaha, N.L. (eds) Surface and Groundwater Resources Development and Management in Semi-arid Region. Springer Hydrogeology. Springer, Cham. https://doi.org/10.1007/978-3-031-29394-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-29394-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29393-1
Online ISBN: 978-3-031-29394-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)