TITLE:
Artificial Intelligence Technique in Hydrological Forecasts Supporting for Water Resources Management of a Large River Basin in Vietnam
AUTHORS:
Truong Van Anh
KEYWORDS:
Hydrological Forecast, Water Resources Management, Machine Learning, Deep Learning, Red River Basin
JOURNAL NAME:
Open Journal of Modern Hydrology,
Vol.13 No.4,
October
25,
2023
ABSTRACT: Hydrological forecasting plays an important role in water resource
management, supporting socio-economic development and managing water-related
risks in river basins. There are many flow forecasting techniques that have
been developed several centuries ago, ranging from physical models,
physics-based models, conceptual models, and data-driven models. Recently,
Artificial Intelligence (AI) has become an advanced technique applied as an
effective data-driven model in hydrological forecasting. The main advantage of
these models is that they give results with compatible accuracy, and require
short computation time, thus increasing forecasting time and reducing human and
financial effort. This study evaluates the applicability of machine learning
and deep learning in Hanoi water level forecasting where it is controlled for
flood management and water supply in the Red River Delta, Vietnam. Accordingly,
SANN (machine learning algorithm) and LSTM (deep learning algorithm) were
tested and compared with a Physics-Based Model (PBM) for the Red River Delta.
The results show that SANN and LSTM give high accuracy. The R-squared
coefficient is greater than 0.8, the mean squared error (MSE) is less than 20
cm, the correlation coefficient of the forecast hydrology is greater than 0.9
and the level of assurance of the forecast plan ranges from 80% to 90% in both
cases. In addition, the calculation time is much reduced compared to the
requirement of PBM, which is its limitation in hydrological forecasting for
large river basins such as the Red River in Vietnam. Therefore, SANN and LSTM
are expected to help increase lead time, thereby supporting water resource
management for sustainable development and management of water-related risks in
the Red River Delta.