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.
Rainfall prediction is a very important guideline for water resources management as well as ecological protection, and its changes are the result of multiple factors with obvious uncertainties and nonlinearities. Based on the advantages of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) non-smooth signal decomposition, the Particle Swarm Optimization (PSO) can be used to optimize the input weights and thresholds of the Extreme Learning Machine (ELM), which can effectively improve the prediction effect and accuracy of ELM, and a rainfall prediction model based on CEEMDAN-PSO-ELM is constructed. The model is applied to the monthly rainfall prediction of Zhongwei city, and the results show that the CEEMDAN-PSO-ELM coupled model has a high prediction accuracy, the mean absolute error (MAE) is 1.29, relative percentage error (RPE) is 0.45, root mean square error (RMSE) is 1.43 and the nash efficiency coefficient (NSE) is 0.93. It has obvious advantages in hydrological simulation prediction when compared and analyzed the deep Long-Short Term Memory (LSTM), PSO-ELM coupled model and ELM model.
The analysis of annual precipitation evolution characteristics is of great value and significance for revealing the spatial and temporal variation patterns of regional precipitation, water resources development and utilization, short-term climate, drought, flood disaster prediction, etc. The MK(Mann-Kendall) mutation test, cumulative distance level method, and Morlet wavelet analysis were used to analyze the precipitation evolution in Anhui Province from 1961 to 2020. The results showed that the average annual temperature and precipitation in Anhui Province showed a significant increasing trend during 1961–2020, with warming and humidification. 1994 was the year of abrupt climate change in Anhui Province, and the temperature after the abrupt change was 2.10 times that before the abrupt change. ENSO (El Niño-Southern Oscillation) has a synchronized resonance cycle with droughts and floods in Anhui Province at 5.8 a. The annual scale of ENSO events is an important theoretical support for regional drought and flood warnings. The chance of drought and flooding in Anhui Province is greater than 50% in the year of ENSO event or two years after the event, and the year of ENSO event or the year after is prone to drought and flooding, so we should strengthen the flood and drought warning, disaster prevention and mitigation.
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