UTILIZING DEEP LEARNING MODELS IN KABIRDHAM, CHHATTISGARH, TO FORECAST AND MODEL RAINFALL
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.2645Keywords:
Rainfall, Deep Learning, ANN, KabirdhamAbstract [English]
Deep learning has emerged as a key area for modeling and forecasting complex time series data. The future performance of Kabirdham rainfall data was investigated in this machine learning project. To construct and validate the model, the dataset is divided into 35% test sets and 65% training sets. We utilized the Root Mean Square Error (RMSE) measure to compare these deep learning models. In this data set, the Modified BPN ANN model performs better than the BILSTM and GRU models. The predictions of these three models are comparable. The development of a comprehensive Kabirdham weather forecast book might benefit from this knowledge. Scholars and policymakers would both benefit from this information. Beyond statistical methods, we think this study can be utilized to apply machine learning to complicated time series data.
References
Beven, K. J., Cloke, H. L., 2012, “Comment on ‘hyper resolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water’ by Eric F. Wood et al.,” Water Resour. Res., 48, 1, 1–10. DOI: https://doi.org/10.1029/2011WR010982
Ehsan khodadadi, S. K. Towfek, Hussein Alkattan. (2023). Brain Tumor Classification Using Convolutional Neural Network and Feature Extraction. Fusion: Practice and Applications, 13(2), 34-41. DOI: https://doi.org/10.54216/FPA.130203
Corzo, G., Solomatine, D., 2007, “Baseflow separation techniques for modular artificial neural network modelling in flow forecasting”, Hydrol. Sci. J., 52, 3, 491–507. DOI: https://doi.org/10.1623/hysj.52.3.491
Al-Nuaimi, B. T., Al-Mahdawi, H. K., Albadran, Z., Alkattan, H., Abotaleb, M., & El-kenawy, E. S. M. (2023). Solving of the inverse boundary value problem for the heat conduction equation in two intervals of time. Algorithms, 16(1), 33. DOI: https://doi.org/10.3390/a16010033
Gursoy, O., Engin, S. N., 2019, “A wavelet neural network approach to predict daily river discharge using meteorological data”, Meas. Control (United Kingdom), 52, 5–6, 599–607. DOI: https://doi.org/10.1177/0020294019827972
Akbari, E., Mollajafari, M., Al-Khafaji, H. M. R., Alkattan, H., Abotaleb, M., Eslami, M., & Palani, S. (2022). Improved salp swarm optimization algorithm for damping controller design for multimachine power system. IEEE Access, 10, 82910-82922. DOI: https://doi.org/10.1109/ACCESS.2022.3196851
Jain, A., Srinivasulu, S., 2006, “Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques”, J. Hydrol., 317, 3–4, 291–306. DOI: https://doi.org/10.1016/j.jhydrol.2005.05.022
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., Herrnegger, M., 2018, “Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks”, 1–26. DOI: https://doi.org/10.5194/hess-2018-247
Al-Mahdawi, H. K., Albadran, Z., Alkattan, H., Abotaleb, M., Alakkari, K., & Ramadhan, A. J. (2023, December). Using the inverse Cauchy problem of the Laplace equation for wave propagation to implement a numerical regularization homotopy method. AIP Conference Proceedings (Vol. 2977, No. 1). AIP Publishing. 9 BIO Web of Conferences 97, 00126 (2024) https://doi.org/10.1051/bioconf/20249700126 ISCKU 2024 DOI: https://doi.org/10.1063/5.0182088
Lindsay, G. W., 2021, “Convolutional neural networks as a model of the visual system: Past, present, and future”, J. Cogn. Neurosci., 33, 10, 2017–2031. DOI: https://doi.org/10.1162/jocn_a_01544
Liu, M., et al., 2020, “The applicability of lstm-knn model for real-time flood forecasting in different climate zones in China”, Water (Switzerland), DOI: https://doi.org/10.3390/w12020440
, 1–21. 12. Parkes, B. L., Wetterhall, F., Pappenberger, F., He, Y., Malamud, B. D., Cloke H. L., 2013, “Assessment of a 1-hour gridded precipitation dataset to drive a hydrological model: A case study of the summer 2007 floods in the upper severn, UK”, Hydrol. Res., 44, 1, 89–105. DOI: https://doi.org/10.2166/nh.2011.025
Poornima, S., Pushpalatha, M., 2019, “Prediction of rainfall using intensified LSTM based recurrent Neural Network with Weighted Linear Units’, Atmosphere (Basel)., 10,11. DOI: https://doi.org/10.3390/atmos10110668
Sang, Y. F., 2013, “A review on the applications of wavelet transform in hydrology time series analysis”, Atmos. Res., 122, 8–15. DOI: https://doi.org/10.1016/j.atmosres.2012.11.003
The Best Time to Visit Chelyabinsk, Russia for Weather, Safety, & Tourism, champion Traveler, https://trek.zone/en/russia/places/18580/chelyabinsk.
Weather and Topography of Chelyabinsk (The weather year-round anywhere on earth), Weather Spark, https://weatherspark.com/y/106113/Average-Weather-in-Chelyabinsk-Russia-Year-Round.
Wesemann, J., Herrnegger, M.,Schulz, K., 2018, “Erratum to: Hydrological modelling in the anthroposphere: predicting local runoff in a heavily modified high-alpine catchment,” J. Mt. Sci., p. 1. DOI: https://doi.org/10.1007/s11629-018-4979-1
Wong, K. W., Wong, P. M., Gedeon, T. D., Fung, C. C., 2003, “Rainfall prediction model using soft computing technique”, Soft Comput., 7, 6, 434–438. DOI: https://doi.org/10.1007/s00500-002-0232-4
Xiang, Z., Yan, J., Demir, I., 2020, “A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning”, Water Resour. Res., 56, 1. DOI: https://doi.org/10.1029/2019WR025326
Zhang, B., Govindaraju, R. S., 2000, “Prediction of watershed runoff using Bayesian concepts and modular neural networks”, Water Resour. Res., 36, 3, 753–762. DOI: https://doi.org/10.1029/1999WR900264
Zolotokrylin, A. N., Vinogradova, V. V., Titkova, T. B., Cherenkova, E. A., Bokuchava, D. D., Sokolov, I. A., Vinogradov, A. V., Babina, E. D., 2018,“Impact of climate changes on population vital activities in Russia in the early 21stcentury”. IOP Conf Ser: Earth and Environ Sci, 107:012045. https://doi.org/10.1088/1755-1315/107/1/012045 DOI: https://doi.org/10.1088/1755-1315/107/1/012045
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Jaleshwar Kaushik, Dr. Omprakash Chandrakar, Dr. Bakhtawer Shameem
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
