## 1 Introduction  
Recently, flood damage has increased at riversides because the water surface elevation is rising rapidly as the occurrence of extreme flooding has increased due to global warming and climate change [1, 2]. As an example of flood damage to riversides, flooding occurred in roads, parks and parking lots and caused damage to life and property [3]. Therefore, it was necessary to immediately forecast and alert about the possibility of the occurrence of flood damage on riversides caused by flash floods. For forecasting flooding that occurs along a riverside, many countries have provided flood forecasting system services such as the Advanced Hydrologic Prediction Service (AHPS in United States), and the Evaluation et Suivi des Phuies en Agglomeration pour Devancer I'Alerte (ESPADAin France) by using hydrological data [4]. In general, the precipitation data have been used for a precipitation-runoff model and the water surface elevation in rivers has been forecasted sequentially. As the final step, the water surface elevation of the river has been used to determine whether flood damage occurs at riverside [5]. This is a standardized flood forecasting process around the world, including Korea. Therefore, accurate forecasting of the water surface elevation has been most significant to forecast a flood event. There are typically two methods for forecasting the water surface elevation of a river. The first one is based on a numerical model which can analyze various differential equations such as Navier-Stokes equations and calculates the water surface elevation by using geometry data of a river, discharge and precipitation, etc. However, a numerical analysis by computational fluid dynamics (CFD) has provided accurate and sophisticated forecasting results, whereas it is difficult to acquire related essential data such as geometry data before a simulation and a simulation takes more time when 2D or 3D results are expected. Therefore, the numerical model had a limitation in forecasting and warning floods within limited time [6]. Recently, the forecasting of water surface elevation was performed by using a data-driven model based on the statistical relationship between the input data and the output result as a large amount of data has been collected and big data processing technology has been developed. Examples of data-driven models applied in the forecasting of water surface elevation are statistical models which include linear regression, autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models and machine learning (ML) models such as artificial neural networks (ANN) [7]. In addition, the ML models were good at performance for forecasting time series data such as water surface elevation compared with other models [8]. In addition, the data-driven model using the artificial neural network has the advantage because it is easy to acquire data when compared to physical models and the time required for forecasting water surface elevation was short, although the ANN had problems such as dependence on the state of the input data, unexplained behavior of the network, etc. Therefore, the data-driven model using ANN was developed to forecast the water surface elevation in this study. Various artificial neural network models were applied for forecasting the water surface elevation. The long short-term memory (LSTM) showed a good performance for forecasting the water surface elevation. However, the LSTM tends to under-forecast at a high water-surface elevation and has a limitation in that the accuracy of forecasting decreases as the leading time becomes longer. Nevertheless, research which solved these problems have been insufficient. In general, the activation functions used in LSTM include the sigmoid function, the hyperbolic tangent function and the rectified linear unit function, but each activation function causes problems such as a vanishing gradient and a dying neuron [9; 10; 11].