Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches
This research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. This method assesses persistence and allows for combinatorial probability estimations such as initial and...
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KeAi Communications Co. Ltd.
2024-12-01
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| Series: | Artificial Intelligence in Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544124000108 |
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| author | Pankaj Chauhan Muhammed Ernur Akiner Rajib Shaw Kalachand Sain |
| author_facet | Pankaj Chauhan Muhammed Ernur Akiner Rajib Shaw Kalachand Sain |
| author_sort | Pankaj Chauhan |
| collection | DOAJ |
| description | This research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. This method assesses persistence and allows for combinatorial probability estimations such as initial and transitional probabilities. The hydrologic data was generated (in-situ) and received from Uttarakhand Jal Vidut Nigam Limited (UJVNL), and meteorological data was acquired from NASA's archives MERRA-2 product. A total of sixteen years (2005–2020) of data was used to foresee daily Precipitation from 2020 to 2022. MERRA-2 products are utilized as observed and forecast values for daily Precipitation throughout the monsoon season, which runs from July to September. Markov Chain and Long Short-Term Memory (LSTM) findings for 2020, 2021, and 2022 were observed, and anticipated values for daily rainfall during the monsoon season between July and September. According to test findings, the artificial intelligence technique cannot anticipate future regional meteorological formations; the correlation coefficient R2 is around 0.12. According to the randomly verified precipitation data findings, the Markov Chain model has a success rate of 79.17 percent. The results suggest that extended return periods should be a warning sign for drought and flood risk in the Himalayan region. This study gives a better knowledge of the water budget, climate change variability, and impact of global warming, ultimately leading to improved water resource management and better emergency planning to the establishment of the Early Warning System (EWS) for extreme occurrences such as cloudbursts, flash floods, landslides hazards in the complex Himalayan region. |
| first_indexed | 2024-03-08T03:34:47Z |
| format | Article |
| id | doaj.art-070f607e935c4807bad24e8b3bf51f5e |
| institution | Directory Open Access Journal |
| issn | 2666-5441 |
| language | English |
| last_indexed | 2024-03-08T03:34:47Z |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Geosciences |
| spelling | doaj.art-070f607e935c4807bad24e8b3bf51f5e2024-02-10T04:45:35ZengKeAi Communications Co. Ltd.Artificial Intelligence in Geosciences2666-54412024-12-015100069Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approachesPankaj Chauhan0Muhammed Ernur Akiner1Rajib Shaw2Kalachand Sain3Wadia Institute of Himalayan Geology, Dehradun, Uttarakhand, 248001, India; Graduate School of Media and Governance, Keio University, 5322 Endo, Fujisawa 252-0882, Kanagawa, Japan; Corresponding author. Wadia Institute of Himalayan Geology, Dehradun, Uttarakhand, 248001, India.Akdeniz University, Vocational School of Technical Sciences, Department of Environmental Protection Technologies, Antalya, TurkeyGraduate School of Media and Governance, Keio University, 5322 Endo, Fujisawa 252-0882, Kanagawa, JapanWadia Institute of Himalayan Geology, Dehradun, Uttarakhand, 248001, IndiaThis research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. This method assesses persistence and allows for combinatorial probability estimations such as initial and transitional probabilities. The hydrologic data was generated (in-situ) and received from Uttarakhand Jal Vidut Nigam Limited (UJVNL), and meteorological data was acquired from NASA's archives MERRA-2 product. A total of sixteen years (2005–2020) of data was used to foresee daily Precipitation from 2020 to 2022. MERRA-2 products are utilized as observed and forecast values for daily Precipitation throughout the monsoon season, which runs from July to September. Markov Chain and Long Short-Term Memory (LSTM) findings for 2020, 2021, and 2022 were observed, and anticipated values for daily rainfall during the monsoon season between July and September. According to test findings, the artificial intelligence technique cannot anticipate future regional meteorological formations; the correlation coefficient R2 is around 0.12. According to the randomly verified precipitation data findings, the Markov Chain model has a success rate of 79.17 percent. The results suggest that extended return periods should be a warning sign for drought and flood risk in the Himalayan region. This study gives a better knowledge of the water budget, climate change variability, and impact of global warming, ultimately leading to improved water resource management and better emergency planning to the establishment of the Early Warning System (EWS) for extreme occurrences such as cloudbursts, flash floods, landslides hazards in the complex Himalayan region.http://www.sciencedirect.com/science/article/pii/S2666544124000108Forecast disastersWestern HimalayaHydro-meteorological hazardsLSTMMarkov chainYamuna river basin |
| spellingShingle | Pankaj Chauhan Muhammed Ernur Akiner Rajib Shaw Kalachand Sain Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches Artificial Intelligence in Geosciences Forecast disasters Western Himalaya Hydro-meteorological hazards LSTM Markov chain Yamuna river basin |
| title | Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches |
| title_full | Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches |
| title_fullStr | Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches |
| title_full_unstemmed | Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches |
| title_short | Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches |
| title_sort | forecast future disasters using hydro meteorological datasets in the yamuna river basin western himalaya using markov chain and lstm approaches |
| topic | Forecast disasters Western Himalaya Hydro-meteorological hazards LSTM Markov chain Yamuna river basin |
| url | http://www.sciencedirect.com/science/article/pii/S2666544124000108 |
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