scholarly journals Precipitation forecast based on CEEMD-LSTM coupled model

2021 ◽  
Author(s):  
Xianqi Zhang ◽  
Xilong Wu ◽  
Shaoyu He ◽  
Dong Zhao
Keyword(s):  
Network Model ◽  
Short Term Memory ◽  
Back Propagation ◽  
Precipitation Forecast ◽  
Precipitation Process ◽  
Monthly Precipitation ◽  
Efficiency Coefficient ◽  
Short Term ◽  
Term Memory ◽  
Precipitation Prediction

Abstract Precipitation forecasting is an important guide to the prevention and control of regional droughts and floods, the rational use of water resources and the ecological protection. The precipitation process is extremely complex and is influenced by the intersection of many variables, with significant randomness, uncertainty and non-linearity. Based on the advantages that Complementary ensemble empirical modal decomposition (CEEMD) can effectively overcome modal aliasing, white noise interference, and the ability of Long Short-Term Memory (LSTM) networks to handle problems such as gradient disappearance. A CEEMD-LSTM coupled long & short-term memory network model was developed and adopted for monthly precipitation prediction of Zhengzhou city. The performance shows that the CEEMD-LSTM model has a mean absolute error of 0.056, a root mean square error of 0.153, a mean relative error of 2.73% and a Nash efficiency coefficient of 0.95, which is better than the CEEMD- Back Propagation (BP) neural network model, the LSTM model and the BP model in terms of prediction accuracies. This demonstrates its powerful non-linear and complex process learning capability in hydrological factor simulation for regional precipitation prediction.

scholarly journals Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 977 ◽  
Author(s):  
Qinghua Miao ◽  
Baoxiang Pan ◽  
Hao Wang ◽  
Kuolin Hsu ◽  
Soroosh Sorooshian
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Precipitation Forecast ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Precipitation Prediction ◽  
Downscaling Method ◽  
Forecast Lead Time ◽  
Long Short Term Memory

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for the monsoon region. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. This superiority decreases with the forecast lead time, as the GCM’s skill in predicting atmospheric dynamics is diminished by the chaotic effect. Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is only slightly worse than the observed precipitation forced simulation (NSE = 0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts.

scholarly journals Supervised Mover's Distance: A simple model for sentence comparison

2018 ◽  
Author(s):  
Muktabh Mayank Srivastava
Keyword(s):  
Neural Network ◽  
Simple Model ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Network Module ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Simple Neural Network

We propose a simple neural network model which can learn relation between sentences by passing their representations obtained from Long Short Term Memory(LSTM) through a Relation Network. The Relation Network module tries to extract similarity between multiple contextual representations obtained from LSTM. Our model is simple to implement, light in terms of parameters and works across multiple supervised sentence comparison tasks. We show good results for the model on two sentence comparison datasets.

scholarly journals Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1500 ◽  
Author(s):  
Halit Apaydin ◽  
Hajar Feizi ◽  
Mohammad Taghi Sattari ◽  
Muslume Sevba Colak ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Energy Production ◽  
Short Term Memory ◽  
Efficiency Coefficient ◽  
Dam Reservoir ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.

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