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

• Autorzy: Srivastava Gautam , Kavitha V. , Vimaladevi M. , Parthiban N.

Identyfikatory

DOI

10.1007/s11600-022-00942-0

• 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.

Słowa kluczowe

EN

soil moisture prediction; sensor technology; deep learning; GRU; FFNN; tropical cyclones

PL

przewidywanie wilgotności gleby; technologia sensorowa; głęboka nauka; GRU; FFNN; cyklony tropikalne

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 6
• Strony: 2933--2951
• Opis fizyczny: Bibliogr. 36 poz.

Twórcy

autor

Srivastava Gautam

• 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

• https://orcid.org/0000-0001-9851-4103

autor

Kavitha V.

• Department of Datascience and Business Systems, SRM Institute of Science and Technology, Kattankulathur, India

autor

Vimaladevi M.

• Department of CSE, KSR Institute for Engineering and Technology, Thiruchengode, India

autor

Parthiban N.

• Department of Datascience and Business Systems, SRM Institute of Science and Technology, Kattankulathur, India

Bibliografia

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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).