Evaluation of Water Resources, Land Use and Land Cover for Sabarmati & Mahi River Basins with Rainfall Runoff Potential Based Modelling Using Computing Techniques & Novel TBBO (Shruti-A) Approach

Evaluation of Water Resources, Land Use and Land Cover for Sabarmati & Mahi River Basins with Rainfall Runoff Potential Based Modelling Using Computing Techniques & Novel TBBO (Shruti-A) Approach
  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-9
Year of Publication : 2024
Author : Utkarsh Nigam, Vinodkumar M. Patel, Dhruvesh P. Patel, Ghanshyam Vadodaria
DOI : 10.14445/22315381/IJETT-V72I9P113

How to Cite?
Utkarsh Nigam, Vinodkumar M. Patel, Dhruvesh P. Patel, Ghanshyam Vadodaria, "Evaluation of Water Resources, Land Use and Land Cover for Sabarmati & Mahi River Basins with Rainfall Runoff Potential Based Modelling Using Computing Techniques & Novel TBBO (Shruti-A) Approach," International Journal of Engineering Trends and Technology, vol. 72, no. 9, pp. 154-172, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I9P113

Abstract
Designing and Innovation issues are extremely dubious and precarious in nature. Especially when questionable boundaries like precipitation, environment, and traffic are concerned, irregularity and ludicrousness are expanded to the top level. Here in the present study, an investigation of the Water assets, land cover and use of the watersheds is done. Sabarmati Bowl and Mahi Stream watershed are executed and performed. Rainfall and runoff anticipating have been performed for the review area, of which the daily precipitation data are arranged from SWDC and digital datasets. Many methodologies have been produced for the turn of events and improvement of precipitation gauge models inside the water designing space. Brain organization (as artificial intelligence or ML), delicate processing, and other powerful models, such as TLBO, further developed TLBO have been created in the past for creating and further developing models. In a water asset designing space, in the event that the model boundary is unsure and shaky, the issues should be created in black box estimating models. In the present review, a methodology is proposed that will surely and astoundingly improve model execution or boundary attributes. Utilizing contextual analyses, it has been shown that there is a major improvement in the model improvement area, which prompts better execution of the models by further developing boundaries. The methodology is named TBBO (Tuition teaching-based Optimization or SHRUTI-A Algorithm). This approach is an improvement towards the improvement of created models and others to foster another model. Examination and contextual analyses show that the model exhibition improvement is finished up to a few times, and the model qualities coefficient of Connection is worked on by 10 to 30 percent. For the monthly models (Best models work out results), the NLR model has an R2 of 0.79 and RMSE of 1.214. ANN model has an R2 of 0.89 and an RMSE of 0.812. FL model has an R2 of 0.845 and an RMSE of 0.772. TBBO model has an R2 of 0.832 and an RMSE of 0.9221. For the yearly analysis performed, the NLR model has an R2 of 0.735 and an RMSE of 1.6. ANN model has an R2 of 0.87 and an RMSE of 0.712. FL model has an R2 of 0.821 and an RMSE of 0.615. TBBO model has an R2 of 0.81 and RMSE of 0.9324. These error analyses show that models developed perform well in which ANN is best suited for training the data sets. It is concluded that the constructed neural network model was capable of quite accurately predicting runoff for the catchments. This approach is great for specialists who are tackling design issues in water assets, as proposed in the event of the review.

Keywords
Watershed, Hydrology, LULC, Rainfall, TBBO, Modelling.

References

[1] M.J. Aitkenhead, and I.H. Aalders, “Predicting Land Cover Using GIS, Bayesian and Evolutionary Algorithm Methods,” Journal of Environmental Management, vol. 90, no. 1, pp. 236-250, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Laurent Ahiablame et al., “Streamflow Response to Potential Land Use and Climate Changes in the James River Watershed, Upper Midwest United States,” Journal of Hydrology: Regional Studies, vol. 14, pp. 150-166, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Yikalo H. Araya, and Pedro Cabral, “Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal,” Remote Sensing, vol. 2, no. 6, pp. 1549-1563, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Mukunda D. Behera et al., “Modelling and Analyzing the Watershed Dynamics Using Cellular Automata (CA)–Markov Model–A Geo-Information Based Approach,” Journal of Earth System Science, vol. 121, no. 4, pp. 1011-1024, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Amna Butt et al., “Land Use Change Mapping and Analysis Using Remote Sensing and GIS: A Case Study of Simly Watershed, Islamabad, Pakistan,” The Egyptian Journal of Remote Sensing and Space Sciences, vol. 18, no. 2, pp. 251-259, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Keith Beven, Rainfall-Runoff Modelling: The Primer, John Wiley & Sons, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[7] N.K. Bhagat, “Rainfall-Runoff Co-Relationship for Lower Mahi Basin,” International Journal of Hydrology, vol. 1, no. 5, pp. 127-130, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Tak Kwin Chang et al., “Rainfall-Runoff Modelling Using a Self-Reliant Fuzzy Inference Network with Flexible Structure,” Journal of Hydrology, vol. 564, pp. 1179-1193, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Stephanie R. Clark et al., “Deep Learning for Monthly Rainfall-Runoff Modelling: A Comparison with Classical Rainfall-Runoff Modelling Across Australia,” Hydrology and Earth System Sciences, vol. 28, no. 5, pp. 1191-1213, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Shibani Chourushi, Pradeep P. Lodha, and Indra Prakash, “A Comparative Study of Three Decadal LU/LC Maps of Sabarmati River Basin,” COMPUSOFT, An International Journal of Advanced Computer Technology, vol. 8, no. 8, pp. 3312-3316, 2019.
[Google Scholar] [Publisher Link]
[11] Chunhao Zhu, and Yingkui Li, “Long Term Hydrological Impacts of Land Use/ Land Cover Change from 1984 to 2010 in the Little River Watershed, Tennessee,” International Soil and Water Conservation Research, vol. 2, no. 2, pp. 11-21, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Jyoti Yadav et al., “Green Energy Generation through PEHF–A Blueprint of Alternate Energy Harvesting,” International Journal of Green Energy, vol. 16, no. 3, pp. 242-255, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ankan Jana et al., “Prediction of Land Use Land Cover Changes of a River Basin Using the CA-Markov Model,” Geocarto International, vol. 37, no. 26, pp. 14127-14147, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Md. Inzamul Haque, and Rony Basak, “Land Cover Change Detection Using GIS and Remote Sensing Techniques: A Spatio-Temporal Study on Tanguar Haor, Sunamganj, Bangladesh,” The Egyptian Journal of Remote Sensing and Space Sciences, vol. 20, no. 2, pp. 251-263, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[15] L. Gadrani, G. Lominadze, and M. Tsitsagi, “F Assessment of Landuse/Landcover (LULC) Change of Tbilisi and Surrounding Area Using Remote Sensing (RS) and GIS,” Annals of Agrarian Science, vol. 16, no. 2, pp. 163-169, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[16] P. Mallikarjuna, C.H. Suresh Babu, and A. Jagan Mohan Reddy, “Rainfall—Runoff Modelling Using Artificial Neural Networks,” ISH Journal of Hydraulic Engineering, vol. 15, no. 1, pp. 24-33, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Usman Mohseni, and Sai Bargav Muskula, “Rainfall-Runoff Modeling Using Artificial Neural Network—A Case Study of Purna Sub-Catchment of Upper Tapi Basin, India,” Environmental Sciences Proceedings, vol. 25, no. 1, pp. 1-8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Wana Geyisa Namara, Tamane Adugna Damise, and Fayera Gudu Tufa, “Rainfall Runoff Modeling Using HEC-HMS: The Case of Awash Bello Sub-Catchment, Upper Awash Basin, Ethiopia,” International Journal of Environment, vol. 9, no. 1, pp. 68-86, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Neil McIntyre, Aisha Al-Qurashi, and Howard Wheater, “Regression Analysis of Rainfall–Runoff Data from an Arid Catchment in Oman,” Hydrological Sciences Journal, vol. 52, no. 6, pp. 1103-1118, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[20] M.P. Rajurkar, U.C. Kothyari, and U.C. Chaube, “Artificial Neural Networks for Daily Rainfall—Runoff Modelling,” Hydrological Sciences Journal, vol. 47, no. 6, pp. 865-877, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[21] R.V. Rao, V.J. Savsani, and D.P. Vakharia, “Teaching–Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Sandeep Samantaray, and Abinash Sahoo, “Prediction of Runoff Using BPNN, FFBPNN, CFBPNN Algorithm in Arid Watershed: A Case Study,” International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 24, no. 3, pp. 243-251, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] A.R. Senthil Kumar et al., “Rainfall‐Runoff Modelling Using Artificial Neural Networks: Comparison of Network Types,” Hydrological Processes: An International Journal, vol. 19, no. 6, pp. 1277-1291, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[24] “Government of India: Ministry of Water Resources, Sabarmati Basin,” Central Water Commission, National Remote Sensing Centre, 2014.
[Publisher Link]
[25] “Government of India: Ministry of Water Resources, Mahi Basin,” Central Water Commission, National Remote Sensing Centre, 2014.
[Publisher Link]
[26] Gokmen Tayfur, and Luca Brocca, “Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture,” Water Resources Management, vol. 29, pp. 3519-3533, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Keh-Han Wang, and Abdusselam Altunkaynak, “Comparative Case Study of Rainfall-Runoff Modeling Between SWMM and Fuzzy Logic Approach,” Journal of Hydrologic Engineering, vol. 17, no. 2, pp. 283-291, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Yeshewatesfa Hundecha, Andras Bardossy, and Hans-Werner Werner, “Development of a Fuzzy Logic-Based Rainfall-Runoff Model,” Hydrological Sciences Journal, vol. 46, no. 3, pp. 363-376, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Sabarmati, India Wris. [Online]. Available: https://indiawris.gov.in/wiki/doku.php?id=sabarmati
[30] Mahi, India Wris. [Online]. Available: https://indiawris.gov.in/wiki/doku.php?id=mahi