Long‐range precipitation forecast based on multipole and preceding fluctuations of sea surface temperature

Wu, Xushu; Guo, Shenglian; Qian, Siwei; Wang, Zhaoli; Lai, Chengguang; Li, Jun; Liu, Pan

doi:10.1002/joc.7690

Cited by 57 publications

(16 citation statements)

References 71 publications

(96 reference statements)

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“…The prediction result of the proposed model had an improvement about 0.3 over that of the previous study [35], where the study area and the resolution of data were basically the same. Wu et al [61] predicted monthly rainfall in the upper and middle Yangtze River basin using the multipole SST anomaly model (MSST), and the August prediction result was lower than that for June and July. Compared to results of Wu et al [61], the prediction results of this study were steadier than those from the MSST-PFMS model.…”

Section: Forecast Results Of the Proposed Modelmentioning

confidence: 99%

“…Wu et al [61] predicted monthly rainfall in the upper and middle Yangtze River basin using the multipole SST anomaly model (MSST), and the August prediction result was lower than that for June and July. Compared to results of Wu et al [61], the prediction results of this study were steadier than those from the MSST-PFMS model.…”

Section: Forecast Results Of the Proposed Modelmentioning

confidence: 99%

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A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

Yang

Chen

Singh

et al. 2023

Water

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Monthly extreme precipitation (EP) forecasts are of vital importance in water resources management and storage behind dams. Machine learning (ML) is extensively used for forecasting monthly EP, and improvements in model performance have been a popular issue. The innovation of this study is summarized as follows. First, a distance correlation-Pearson correlation (DC-PC) method was proposed to identify the complex nonlinear relationship between global sea surface temperature (SST) and EP and select key input factors from SST. Second, a random forest (RF) model was used for forecasting monthly EP, and the physical mechanism of EP was obtained based on the feature importance (FI) of RF and DC–PC relationship. The middle and lower reaches of the Yangtze River (MLYR) were selected as a case study, and monthly EP in summer (June, July and August) was forecasted. Furthermore, the physical mechanism between key predictors with a large proportion of FI and EP was investigated. Results showed that the proposed model had high accuracy and robustness, in which R2 in the test period was above 0.81, and RMSE as well as MAE were below 10 mm. Meanwhile, the key predictors in the high SST years could cause eastward extension of the South Asian High, westward extension of the Western Pacific Subtropical High, water vapor rising motion and an increase in the duration of atmospheric rivers exceeding 66 h, which lead to increasing EP in the MLYR. The results indicated that the DC–PC method could replace Pearson correlation for investigating the nonlinear relationship between SST and EP, as well as for selecting the factors. Further, the key predictors that account for a large proportion of FI can be used for explaining the physical mechanism of EP and directing forecasts.

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Section: Forecast Results Of the Proposed Modelmentioning

confidence: 99%

Section: Forecast Results Of the Proposed Modelmentioning

confidence: 99%

A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

Yang

Chen

Singh

et al. 2023

Water

View full text Add to dashboard Cite

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“…Step (3): Localization of olfaction; substituting the taste concentration judgment value S i in step (2) into fitness function to find the taste concentration of each individual position of the fruit fly Smell i , and find out the fruit fly with the best taste concentration in the fruit fly population (find the maximum value) [39][40][41]:…”

Section: Fruit Fly Optimization Algorithmmentioning

confidence: 99%

Combined Forecasting Model of Precipitation Based on the CEEMD-ELM-FFOA Coupling Model

Zhang

2023

Water

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Precipitation prediction is an important technical mean for flood and drought disaster early warning, rational utilization, and the development of water resources. Complementary ensemble empirical mode decomposition (CEEMD) can effectively reduce mode aliasing and white noise interference; extreme learning machines (ELM) can predict non-stationary data quickly and easily; and the fruit fly optimization algorithm (FFOA) has better local optimization ability. According to the multi-scale and non-stationary characteristics of precipitation time series, a new prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), extreme learning machine (ELM), and the fruit fly optimization algorithm (FFOA) is proposed. The monthly precipitation data measured in Zhengzhou City from 1951 to 2020 was taken as an example to conduct a prediction experiment and compared with three prediction models: ELM, EMD-HHT, and CEEMD-ELM. The research results show that the sum of annual precipitation predicted by the CEEMD-ELM-FFOA model is 577.33 mm, which is higher than the measured value of 572.53 mm with an error of 4.80 mm. The average absolute error is 0.81 and the average relative error is 1.39%. The prediction value of the CEEMD-ELM-FFOA model can closely follow the changing trend of precipitation, which shows a better prediction effect than the other three models and can be used for regional precipitation prediction.

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“…A little less than half of the flood pixels are found in soil group B, whereas the remaining 30.8% are found in soil group C. This dataset on rainfall was acquired from the National Meteorological Administration by analyzing the weather stations linked to the service. Rainfall is one of the leading predictors of flood phenomena, which is directly linked to the genesis of floods (Wu et al., 2022; Zhu et al., 2023). We used the spline method to interpolate the 23 rainfall values gathered from the weather station.…”

Section: Datamentioning

confidence: 99%

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

et al. 2023

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Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute—weights of evidence (forest‐PA‐WOE), best first decision tree—WOE, alternating decision tree—WOE, and logistic regression—WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest‐PA‐WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data‐scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

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Long‐range precipitation forecast based on multipole and preceding fluctuations of sea surface temperature

Cited by 57 publications

References 71 publications

A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

Combined Forecasting Model of Precipitation Based on the CEEMD-ELM-FFOA Coupling Model

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

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