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Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation
Author(s) -
Gui Hanliang,
Wu Zhiguo,
Zhang Chunping
Publication year - 2021
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.202000381
Subject(s) - autoregressive integrated moving average , autoregressive model , computer science , hydrological modelling , simulation modeling , process (computing) , artificial neural network , mathematical model , hydrology (agriculture) , data mining , time series , statistics , machine learning , mathematics , engineering , geology , climatology , geotechnical engineering , mathematical economics , operating system
The hydrological model is one of the key methods in hydrological research and management, which can be divided into physical model and mathematical model. In order to explore the applicability of different models in hydrological simulation, the physical model Hydrologic Simulation Program‐FORTRAN (HSPF), the mathematical models Autoregressive Integrated Moving Average (ARIMA) and Back Propagation Neural Network (BPNN) are built to simulate the hydrological process of Sanya River, China. First, the result shows that all three models have good performance, but the HSPF model has the best performance followed by the ARIMA and the BPNN model with the lowest performance. Next, the effect of time step on the performance of the BPNN and ARIMA models is obviously greater than for the HSPF model. Finally, the applicability of various models is discussed by combining the modeling process and data requirements of the model, and the applicability of the mathematical model in the hydrological simulation of watersheds with insufficient data is confirmed.