ANN and GRNN-Based Coupled Model for Flood Inundation Mapping of the Punpun River Basin
Received: 13 November 2022 | Revised: 22 November 2022 | Accepted: 25 November 2022 | Online: 5 February 2023
Corresponding author: Shashi Ranjan
Abstract
The Punpun River is primarily a rain-fed river. Forecasting rainfall accurately would enable an early evaluation of drought and flooding conditions. Therefore, having a flawless model for predicting rainfall is important for the hydrological analysis of any river basin. In this study, Artificial Neural Network (ANN)-based models were developed to predict rainfall and discharge in the basin. During the rainy season, water is spread in and around the area of the watershed, thus a General Regression Neural Network (GRNN)-based model was proposed for fast estimation of the inundation area during the flood taking as input cross-section, rainfall, and discharge. The proposed ANN-GRNN coupled model is the first of its kind for this study area. The assessment of the results shows that the proposed GRNN-based model is capable of estimating the water-spreading area.
Keywords:
ANN, GRNN, rainfall, flood plain, Punpun river basinDownloads
References
S. Rana, V. Deoli, and S. R. Chavan, "Detection of abrupt change in trends of rainfall and rainy day’s pattern of Uttarakhand," Arabian Journal of Geosciences, vol. 15, no. 7, Mar. 2022, Art. no. 618. DOI: https://doi.org/10.1007/s12517-022-09883-w
V. Deoli and S. Rana, "Seasonal trend analysis in rainfall and temperature for Udaipur district of Rajasthan.," Current World Environment, vol. 14, no. 2, pp. 312–319, 2019. DOI: https://doi.org/10.12944/CWE.14.2.15
A. Towheed and R. Thendiyath, "Spatiotemporal Rainfall Dynamics in Kosi Basin Using Wavelet Analysis," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7578–7584, Oct. 2021. DOI: https://doi.org/10.48084/etasr.4372
A. Towheed and T. Roshni, "Linking climate change to soil loss estimation in the Kosi river basin, India," Journal of Water and Climate Change, vol. 12, no. 6, pp. 2338–2363, Mar. 2021. DOI: https://doi.org/10.2166/wcc.2021.259
K. Praveen and L. B. Roy, "Assessment of Groundwater Quality Using Water Quality Indices: A Case Study of Paliganj Distributary, Bihar, India," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8199–8203, Feb. 2022. DOI: https://doi.org/10.48084/etasr.4696
A. Rawat, P. Kumar, and V. Deoli, "Daily Monsoon Rainfall Prediction using Artificial Neural Network (ANN) for Parbhani District of Maharashtra," International Journal of Current Microbiology and Applied Sciences, vol. 8, no. 12, pp. 1949–1963, Dec. 2019. DOI: https://doi.org/10.20546/ijcmas.2019.812.233
P. Esling and C. Agon, "Time-series data mining," ACM Computing Surveys, vol. 45, no. 1, Sep. 2012, Art. no. 12. DOI: https://doi.org/10.1145/2379776.2379788
Y. Dash, S. K. Mishra, and B. K. Panigrahi, "Rainfall prediction for the Kerala state of India using artificial intelligence approaches," Computers & Electrical Engineering, vol. 70, pp. 66–73, Aug. 2018. DOI: https://doi.org/10.1016/j.compeleceng.2018.06.004
A. Raja and G. T, "Drought Prediction and Validation for Desert Region using Machine Learning Methods," International Journal of Advanced Computer Science and Applications, vol. 13, no. 7, pp. 47–53, 2022. DOI: https://doi.org/10.14569/IJACSA.2022.0130707
C. Sireesha, T. Roshni, and M. K. Jha, "Insight into the precipitation behavior of gridded precipitation data in the Sina basin," Environmental Monitoring and Assessment, vol. 192, no. 11, Oct. 2020, Art. no. 729. DOI: https://doi.org/10.1007/s10661-020-08687-3
N. Q. Hung, M. S. Babel, S. Weesakul, and N. K. Tripathi, "An artificial neural network model for rainfall forecasting in Bangkok, Thailand," Hydrology and Earth System Sciences, vol. 13, no. 8, pp. 1413–1425, Aug. 2009. DOI: https://doi.org/10.5194/hess-13-1413-2009
L. Zhao, F. E. Hicks, and A. R. Fayek, "Applicability of multilayer feed-forward neural networks to model the onset of river breakup," Cold Regions Science and Technology, vol. 70, pp. 32–42, Jan. 2012. DOI: https://doi.org/10.1016/j.coldregions.2011.08.011
A. Ehret, D. Hochstuhl, D. Gianola, and G. Thaller, "Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle," Genetics Selection Evolution, vol. 47, no. 1, Mar. 2015, Art. no. 22. DOI: https://doi.org/10.1186/s12711-015-0097-5
D. Yadav, R. Naresh, and V. Sharma, "Stream flow forecasting using Levenberg-Marquardt algorithm approach," International Journal of Water Resources and Environmental Engineering, vol. 3, no. 1, pp. 30–40, Jan. 2011.
D. Kumar and R. K. Bhattacharjya, "GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data," Journal of Hydroinformatics, vol. 23, no. 3, pp. 567–588, Mar. 2021. DOI: https://doi.org/10.2166/hydro.2021.108
T. Roshni, M. K. Jha, R. C. Deo, and A. Vandana, "Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System," Water Resources Management, vol. 33, no. 7, pp. 2381–2397, May 2019. DOI: https://doi.org/10.1007/s11269-019-02253-4
S. A. Hannan, R. R. Manza, and R. J. Ramteke, "Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis," International Journal of Computer Applications, vol. 7, no. 13, pp. 7–13, Oct. 2010. DOI: https://doi.org/10.5120/1325-1799
K. R. K. Reddy and V. Singh, "Numerical Model to Simulate Soil Wetting Pattern under Drip Irrigation System," Agricultural Science Digest, Aug. 2022. DOI: https://doi.org/10.18805/ag.D-5619
S. Ranjan and V. Singh, "HEC-HMS based rainfall-runoff model for Punpun river basin," Water Practice and Technology, vol. 17, no. 5, pp. 986–1001, Apr. 2022. DOI: https://doi.org/10.2166/wpt.2022.033
Q. Zhang, P. Zhang, and X. Hu, "Unsupervised GRNN flood mapping approach combined with uncertainty analysis using bi-temporal Sentinel-2 MSI imageries," International Journal of Digital Earth, vol. 14, no. 11, pp. 1561–1581, Nov. 2021. DOI: https://doi.org/10.1080/17538947.2021.1953160
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