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Summary of "Trend Analysis and Forecasting of Streamflow in the Upper Narmada Basin using Random Forest (RF) and Long Short-Term Memory (LSTM) Models"

Summary of my EGU 2023 General Assembly Presentation:

In our study, we investigated change point detection, trend analysis, and streamflow forecasting for Upper Narmada Basin We presented our findings at the EGU 2023 General Assembly, covering the following key points:


Overview of Results:

1. Change Point Detection:
  • Consistent change points detected by Pettitt's test and SNHT
  • Significant alterations in streamflow levels were identified for each river station
2. Trend Analysis:
  • Annual, seasonal, and monthly trends were analyzed using MK, MMK tests, and Sen's Slope method
  • No significant trends were found using MK and MMK tests for annual data
  • Varying patterns observed depending on the location and specific streamflow metric (average, maximum, or minimum) for seasonal and monthly data
3. Seasonal and Monthly Streamflow:
  • Barmanghat: significant increasing trend in average and maximum streamflows in January before the change point
  • Belkedi: the significant decreasing trend for maximum streamflow in January and May before the change point, and in several months for minimum streamflow
  • Gadarwara: significant decreasing trends in maximum streamflow in April, May, and June before the change point, and in October and November for minimum streamflow after the change point
  • Manot: significant increasing trend in minimum streamflow in June after the change point
4. Streamflow Forecasting:
  • The LSTM model outperformed the RF model in most cases during training and testing periods
  • Superior performance attributed to LSTM's ability to capture temporal dependencies in time series data
Some plots are attached below 



Figure : Change point detection in Belkhedi 



Figure: Result of training (1990-2000) and testing (2001-2018) of RF and LSTM model target and prediction streamflow in Barmanghat

If you require access to the code used in this study, please feel free to contact me at my email address. I will be more than happy to assist you and share the relevant resources.    Email: Siddik

How to cite: Barbhuiya, S., Ramadas, M., Jena, S., and Biswal, S.: Trend Analysis and Forecasting of Streamflow in the Upper Narmada Basin using Random Forest (RF) and Long Short-Term Memory (LSTM) Models, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10952, https://doi.org/10.5194/egusphere-egu23-10952








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