Analyzing hydro-meteorological data from Yamuna river basin, western Himalaya: Using a Markov Chain and LSTM approach to forecast future disasters

Abstract

This research aims to evaluate hydro-meteorological data from the discharge site at the Dakpathar barrage in 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. Precipitation data for 2021 and 2022 were also retrieved from MERRA-2 products and 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 seen as a warning sign for drought and flood risk in the Himalayan region. This study gives a better knowledge of the water budget and better evaluations of the overall state of the climate change variability, impact for global warning, ultimately leading to improved water resource management and better emergency planning to establishment of the Early Warning System (EWS) for extreme occurrences such as cloudbursts, flash floods, landslides and other hydro-meteorological related hazards in the complex Himalayan region.