Prediction of Yangtze River streamflow based on deep learning neural network with El Niño–Southern Oscillation

Abstract

Accurate long-term streamflow and flood forecasting have always been an important research direction in hydrology research. Nowadays, climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about 1 month in advance, which is too short for decision making. The artificial neural network (ANN) has great potential for predicting runoff and is not only good at handling non-linear data but can also make long-period forecasts. However, most of ANN models are unstable in their predictions when faced with raw flow data, and have excessive errors in predicting extreme flows. Previous studies have shown a link between the El Niño–Southern Oscillation (ENSO) and the streamflow of the Yangtze River. In this paper, we use ENSO and the monthly streamflow data of the Yangtze River from 1952 to 2018 to predict the monthly streamflow of the Yangtze River in two extreme flood years and a small flood year by using deep neural networks. In this paper, three deep neural network frameworks are used: stacked long short-term memory, Conv long short-term memory encoder–decoder long short-term memory and Conv long short-term memory encoder–decoder gate recurrent unit. The results show that the use of ConvLSTM improves the stability of the model and increases the accuracy of the flood prediction. Besides, the introduction of ENSO to the experimental data resulted in a more accurate prediction of the time of the occurrence of flood peaks and flood flows. Furthermore, the best results were obtained on the convolutional long short-term memory + encoder–decoder gate recurrent unit model.

Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML

“…Deep learning (DL) tools are increasingly used in earth sciences and in hydrology in particular to predict precipitation [10][11][12][13][14], streamflow [15][16][17][18][19][20], and groundwater [21][22][23][24][25][26]. These prior applications fall into two categories: spatially based and temporally based.…”

“…Intensity, length, and recession for the rainfall scenarios. Note that the scenarios are color-coded for train(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16), validation(17)(18)(19)(20), and test(21)(22)(23)(24).…”

Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques

“…In future works, more advanced de‐noising methods, such as the wavelet de‐noising approach (Nourani, Baghanam, et al., 2014) or ensemble empirical mode decomposition (EEMD) (Gaci, 2016), are recommended for obtaining better performance in the application of ML models. Moreover, more advanced artificial intelligence models, such as deep learning neural networks (Ha et al., 2021), should be considered in the next attempt to forecast the discharge, sediment, and salinity in the VMD to support strategic decision making. The hybridization of ML models with other empirical, analytical or numerical models is also a good strategy to improve the prediction power of ML.…”

Possible Linkages of Hydrological Variables to Ocean–Atmosphere Signals and Sunspot Activity in the Upstream Yangtze River Basin

“…Gaining a comprehensive understanding of the effects of oceanic-atmospheric climate anomalies and solar activity on temporal variability of precipitation and streamflow in specific watersheds is of great significance for hydrological simulation, climate change and risk management as well as for addressing water-resource-related issues [3,30,31]. Due to its proximity to the Pacific Ocean, the Yangtze River basin is prominently sensitive to such climatic phenomena as El Niño-Southern Oscillation (ENSO) [32][33][34][35][36] and Pacific Decade Oscillation (PDO) [33,35,37,38], which have significant implications for forecasting water resources and climate conditions. In recent years, studies in the Yangtze River basin and its tributaries have made significant advances in the influence of large-scale teleconnection patterns on hydrological variability [33,35,37,[39][40][41][42][43][44][45], while the temporal persistence of these relationships is not yet wholly understood.…”