Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation

Abstract

Streamflow forecasting is essential for hydrological engineering. In accordance with the advancement of computer aids in this field, various machine learning (ML) models have been explored to solve this highly non-stationary, stochastic, and nonlinear problem. In the current research, a newly explored version of an ML model called the long short-term memory (LSTM) was investigated for streamflow prediction using historical data for forecasting for a particular period. For a case study located in a tropical environment, the Kelantan river in the northeast region of the Malaysia Peninsula was selected. The modelling was performed according to several perspectives: (i) The feasibility of applying the developed LSTM model to streamflow prediction was verified, and the performance of the developed LSTM model was compared with the classic backpropagation neural network model; (ii) In the experimental process of applying the LSTM model to the prediction of streamflow, the influence of the training set size on the performance of the developed LSTM model was tested; (iii) The effect of the time interval between the training set and the testing set on the performance of the developed LSTM model was tested; (iv) The effect of the time span of the prediction data on the performance of the developed LSTM model was tested. The experimental data show that not only does the developed LSTM model have obvious advantages in processing steady streamflow data in the dry season but it also shows good ability to capture data features in the rapidly fluctuant streamflow data in the rainy season. INDEX TERMS Deep learning model, streamflow forecasting, tropical environment, window scale forecasting, LSTM.

Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River

“…It can reconstruct the input sequence in the encoder part and forecast the next sequence in the decoder part. The LSTM-based Encoder Decoder (En-De) architecture has been adopted in many fields such as language translation [38], [39], image captioning [40], and speech recognition [41], also streamflow simulation [15]. However, a few studies focus on analyzing data sets of streamflow indices with the LSTM-based En-De architecture.…”

“…Secondly, most classical statistical methods cannot forecast the flood peak discharge precisely, which is the most important motivation for streamflow prediction. Last but not least, most research towards river flow prediction is yearly [11], [12], seasonal [13],daily [14], [15] or even hourly [16]. However, the flood season on the Yangtze River usually lasts 2-3 months within a year.…”

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

“…ese characteristics make the forecasting process challenging for most of the hydrological researchers [8,9]. Accurate long-term forecasting of river flow at monthly and yearly scale is very important for the planning and operation of water reservoir, agricultural and irrigation water management, estimation of catchment water balance, estimating minimum instream environmental flow, and other purposes [10,11]. e accurate short-term (real-time) forecasting of river flow such as hourly or daily time step is important for flood and/or water scarcity forecasting in order to minimize and mitigate their effects on infrastructure and public health [12].…”

The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow

“…Long-term forecasts (e.g., monthly or annual) are useful for several applications, such as irrigation management decisions, reservoir operations, hydro-power generation, and sediment transportation [9], [10]. The accurate streamflow forecasting can contribute to several watershed catchment sustainability and management and thus its accurate forecasting is highly beneficial for decision makers and river engineering maintenance [11], [12].…”