Abstract
It is no doubt that the reliable runoff simulation for proper water resources management is essential. In the past, the runoff was generally modeled from hydrologic models that analyze the rainfall-runoff relationship of the basin. However, since techniques have developed rapidly, it has been attempted to apply especially deep-learning technique for hydrological studies as an alternative to the hydrologic model. The objective of the study is to examine whether the deep-learning technique can completely replace the hydrologic model and show how to improve the performance of runoff simulation using deep-learning technique. The runoff in the Hyeongsan River basin, South Korea from 2013 to 2020 were simulated using two models, (1) long short-term memory model that is a deep learning technique widely used in the hydrological study and (2) TANK model, and then we compared the runoff modeling results from both models. The results suggested that it is hard to completely replace the hydrological model with the deep-learning technique due to its simulating behavior and discussed how to improve the reliability of runoff simulation results. Also, a method to improve the efficiency of runoff simulation through a hybrid model which is a combination of two approaches, deep-learning technique and hydrologic model was presented.
Highlights
One of the biggest issues in engineering field in the late 2010s is deep learning based data driven model
The inflow of the reservoir was predicted through the combined hybrid model of deep belief networks (DBN) and Long short-term memory (LSTM) techniques (Luo et al, 2020), and a study was conducted to build a hybrid model and improve the prediction efficiency by using the runoff data simulated by the VIC-CaMa-Flood model as the input data of the LSTM model (Yang et al, 2019). Based on these results provided from the previous studies, it seems that is it possible to replace the hydrologic model through the deep learning technique as suggested in numerous studies
In the case of the TANK model, additional parameter optimization was performed using a genetic algorithm (GA; Whitley, 1994) based on the model parameters presented in the Hyeongsan River Basic Plan Report (Ministry of Land, Infrastructure and Transport, 2013)
Summary
One of the biggest issues in engineering field in the late 2010s is deep learning based data driven model. Numerous studies have attempted to apply data-driven models such as machine leaning or deep learning techniques for various purposes from water information analysis to hydrological, hydraulic analysis based on vast amount of water-related data (Sit et al, 2020). General hydrologic models conceptualize the physical characteristics of rainfall-runoff relationship by using mathematical equations (Singh, 1998). Since a system conceptualized by human cannot clearly represent the complexity of natural systems (Marçais and Dreuzy, 2017), it cannot be guaranteed that the hydrologic model always simulates the rainfall-runoff relationships well. A statistical model for simulating rainfall-runoff based on hydrological data has been used, but it has limitations that indicate uncertainty and excessive computational power (Ardabili et al, 2019)
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