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The hydrological impact of tropical cyclones on soil moisture using a sensor based hybrid deep learning model

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Tropical cyclones that originate from the Indian Ocean affect the Indian Sub-Continent. Heavy rainfall and flooding occur because of these cyclones. South Odisha was affected by Cyclonic Storm Daye in September 2018 and Cyclonic Storm Titli was occurred in August affecting Andhra Pradesh and Odisha as well. The Eastern portion of India was affected by the Cyclonic Storm Fani in April 2019. In May 2020, West Bengal was affected by the Amphan which is a Super Cyclonic Storm and in the same year Tamil Nadu was affected by the very severe Cyclonic Storm Nivar in November 2020. These are just a few of the notable cyclonic events in the Indian Sub-Continent. These cyclonic events cause a dramatic change in a very short time from dry soil to exceptional flooding. In this proposed work, we are attempting to create an observations-driven prediction model to quantify the soil moisture variations daily, predict county-based meteorology and evaluate the cause of cyclones and heavy rainfall in certain areas of India. In our work, we applied a deep learning-based methodology to predict soil moisture. For the prediction model, we fused Feed Forward Neural Networks with the Gated Recurrent Unit (GRU) model and present the prediction results. We have used climatic as well as environmental data published by the Indian Meteorological Department (IMD) Warning from 2011. The collected data is time-series data. Comparisons and the relationship that exists between soil moisture and meteorological data are made and analyzed. The soil moisture of the South Indian states Karnataka, Andhra Pradesh and Tamil Nadu are predicted from weather data using a hybrid deep learning model. The evaluations of the proposed work using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R-squared (R2 ) against Non-hybrid Neural Network models such as Artificial Neural Networks (ANN), Convolutional Neural Networks, and Gated Recurrent Unit (GRU) models is analyzes where our model has given better results.
Czasopismo
Rocznik
Strony
2933--2951
Opis fizyczny
Bibliogr. 36 poz.
Twórcy
  • Department of Mathematics and Computer Science, Brandon University, Brandon, Canada
  • Research Centre for Interneural Computing, China Medical University, Taichung, Taiwan
  • Department of Computer Science and Math, Lebanese American University, 1102 Beirut, Lebanon
autor
  • Department of Datascience and Business Systems, SRM Institute of Science and Technology, Kattankulathur, India
  • Department of CSE, KSR Institute for Engineering and Technology, Thiruchengode, India
autor
  • Department of Datascience and Business Systems, SRM Institute of Science and Technology, Kattankulathur, India
Bibliografia
  • 1. Ahmed A, Deo RC, Feng Q, Ghahramani A, Raj N, Yin Z, Yang L (2021a) Hybrid deep learning method for a week-ahead evapotranspiration forecasting. Stoch Environ Res Risk Assess 1–19
  • 2. Ahmed U, Lin JCW, Srivastava G, Djenouri Y (2021b) A nutrient recommendation system for soil fertilization based on evolutionary computation. Comput Electron Agric 189:106407
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  • 4. Brezak D, Bacek T, Majetic D, Kasac J, Novakovic B (2012) A comparison of feed-forward and recurrent neural networks in time series forecasting. In: 2012 IEEE Conference on Computational intelligence for financial engineering economics (CIFEr) p. 1–6 IEEE
  • 5. Chen S, Zhang X, Shao L, Sun H, Niu J et al (2015) A comparative study of yield, cost-benefit and water use efficiency between monoculture of spring maize and double crops of wheat-maize under rain-fed condition in the North China Plain. Zhongguo Shengtai Nongye Xuebao Chin J Eco Agric 23(5):535–543
  • 6. Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259
  • 7. Chukalla AD, Krol MS, Hoekstra AY (2015) Green and blue water footprint reduction in irrigated agriculture: effect of irrigation techniques, irrigation strategies and mulching. Hydrol Earth Syst Sci 19(12):4877–4891
  • 8. Corbosiero KL, Molinari J (2003) The relationship between storm motion, vertical wind shear, and convective asymmetries in tropical cyclones. J Atmos Sci 60(2):366–376
  • 9. Dehghani M, Zoej MJV, Entezam I, Saatchi SS, Shemshaki A (2010) Interferometric measurements of ground surface subsidence induced by overexploitation of groundwater. J Appl Remote Sens 4(1):041864
  • 10. Feizizadeh B, Garajeh MK, Lakes T, Blaschke T (2021) A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia lake drought in Iran. CATENA 207:105585
  • 11. Feki M, Ravazzani G, Ceppi A, Milleo G, Mancini M (2018) Impact of infiltration process modeling on soil water content simulations for irrigation management. Water 10(7):850
  • 12. Ghimire S, Deo RC, Raj N, Mi J (2019) Deep learning neural networks trained with modis satellite-derived predictors for long-term global solar radiation prediction. Energies 12(12):2407
  • 13. Gopikrishnan S, Srivastava G, Priakanth P (2022) Improving sugarcane production in saline soils with machine learning and the internet of things. Sustainable Comput Inform Syst 35:100743
  • 14. Gu J, Yin G, Huang P, Guo J, Chen L (2017) An improved back propagation neural network prediction model for subsurface drip irrigation system. Comput Electr Eng 60:58–65
  • 15. Han H, Morrison RR (2021) Data-driven approaches for runoff prediction using distributed data. Stoch Environ Res Risk Assess 8:2153–71
  • 16. Huang C, Li L, Ren S, Zhou Z (2010) Research of soil moisture content forecast model based on genetic algorithm BP neural network. In: International conference on computer and computing technologies in agriculture, p 309–316 Springer
  • 17. Kolstad EW (2021) Prediction and precursors of idai and 38 other tropical cyclones and storms in the mozambique channel. Q J R Meteorol Soc 147(734):45–57
  • 18. Kotz M, Levermann A, Wenz L (2022) The effect of rainfall changes on economic production. Nature 601(7892):223–227
  • 19. Kunkel KE, Champion SM (2019) An assessment of rainfall from hurricanes harvey and florence relative to other extremely wet storms in the United States. Geophys Res Lett 46(22):13500–13506
  • 20. Leng G, Leung LR, Huang M (2016) Irrigation impacts on the water cycle and regional climate simulated by the acme model. In: AGU Fall Meeting Abstracts, 2016, p GC31B–1122
  • 21. Liao R, Yang P, Wu W, Luo D, Yang D (2018) A dna tracer system for hydrological environment investigations. Environ Sci Technol 52(4):1695–1703
  • 22. Prasad R, Deo RC, Li Y, Maraseni T (2018) Soil moisture forecasting by a hybrid machine learning technique: elm integrated with ensemble empirical mode decomposition. Geoderma 330:136–161
  • 23. Pulido-Calvo I, Gutierrez-Estrada JC (2009) Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosys Eng 102(2):202–218
  • 24. Sawaf MBA, Kawanisi K, Jlilati MN, Xiao C, Bahreinimotlagh M (2021) Extent of detection of hidden relationships among different hydrological variables during floods using data-driven models. Environ Monit Assess 193(11):1–14
  • 25. Sturdevant-Rees P, Smith JA, Morrison J, Baeck ML (2001) Tropical storms and the flood hydrology of the central appalachians. Water Resour Res 37(8):2143–2168
  • 26. Tsang S, Jim CY (2016) Applying artificial intelligence modeling to optimize green roof irrigation. Energy Build 127:360–369
  • 27. Uddin MJ, Nasrin ZM, Li Y (2021) Effects of vertical wind shear and storm motion on tropical cyclone rainfall asymmetries over the north indian ocean. Dyn Atmos Oceans 93:101196
  • 28. Villarini G, Goska R, Smith JA, Vecchi GA (2014) North atlantic tropical cyclones and us flooding. Bull Am Meterol Soc 95(9):1381–1388
  • 29. Wang L, Zhuo W, Pei Z, Tong X, Han W, Fang S (2021) Using long-term earth observation data to reveal the factors contributing to the early 2020 desert locust upsurge and the resulting vegetation loss. Remote Sens 13(4):680
  • 30. Warning, C.: Research centre (2011) Tracks of cyclones and depressions over North Indian Ocean (from 1891 onwards). IMD version, Cyclone eAtlas, p 2
  • 31. Wasko C, Nathan R (2019) Influence of changes in rainfall and soil moisture on trends in flooding. J Hydrol 575:432–441
  • 32. Williams A, Hunter MC, Kammerer M, Kane DA, Jordan NR, Mortensen DA, Smith RG, Snapp S, Davis AS (2016) Soil water holding capacity mitigates downside risk and volatility in us rainfed maize: time to invest in soil organic matter? PLoS ONE 11(8):e0160974
  • 33. Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a long short-term memory (lstm) based model for predicting water table depth in agricultural areas. J Hydrol 561:918–929
  • 34. Zhang X, Li R, Jiao M, Zhang Q, Wang Y, Li J (2016) Development of soil moisture monitor and forecast system. Trans Chin Soc Agric Eng 32(18):140–146
  • 35. Zhou X, Wu W, Qin Y, Fu X (2021) Geoinformation-based landslide susceptibility mapping in subtropical area. Sci Rep 11(1):1–16
  • 36. Zhu Q, Luo Y, Xu YP, Tian Y, Yang T (2019) Satellite soil moisture for agricultural drought monitoring: assessment of smap-derived soil water deficit index in Xiang River Basin, China. Remote Sens 11(3):362
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-77798fb0-03ec-4034-89e4-97b574dda4d7
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