scholarly journals Stepwise Identification of Influencing Factors and Prediction of Typhoon Precipitation in Anhui Province Based on the Back Propagation Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 550
Author(s):  
Yuliang Zhou ◽  
Yang Li ◽  
Juliang Jin ◽  
Ping Zhou ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Neural Network Model ◽  
Precipitation Amount ◽  
Back Propagation ◽  
Significant Error ◽  
Validation Period ◽  
Absolute Value ◽  
Mean Relative Error ◽  
The Mean

Typhoon is one of the most frequent meteorological phenomena that covers most of central-eastern China during the summer. Typhoon-induced precipitation is one of the most important water resources, but it often leads to severe flood disasters. Accurate typhoon precipitation prediction is crucial for mitigating typhoon disasters and managing water resources. Anhui Province, located in East China, is a typhoon affected region. Typhoon-related disasters are its major natural disasters. This study aims at developing a new back propagation (BP) neural network model to predict both the typhoon precipitation event and the typhoon precipitation amount. The predictors in the model are identified through correlation analysis of the above two target variables and a large set of candidate variables. We further improve the predictor selection through an iterative approach, which proposes new predictors for the BP model in each iteration by analyzing the differences of candidate predictors between the years with large prediction errors and the normal years. The results show that the accuracy of the BP-based summer typhoon event prediction model in the simulation period from 1957 to 2006 is 100%, and its accuracy in the validation period from 2007 to 2016 is 90%. In addition, the absolute value of the mean relative error predicted by the typhoon precipitation amount model for the simulation period is 20.9%. A significant error can be found in 2000 as the mechanism of typhoon precipitation in this year is different from that of other normal years. The error in 2000 is probably caused by the impact of vertical shear anomalies over the western Pacific which hinders the development of typhoon embryos. Additionally, the absolute value of the mean relative error predicted by the typhoon precipitation amount model in the validation period is 14.2%. A significant error also can be found in 2009, probably due to the influence of the asymmetry in the typhoon cloud system.

scholarly journals Assessment of water resource security in karst area of Guizhou Province, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liying Liu
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Water Resource ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Water Shortage ◽  
Guizhou Province ◽  
Karst Area ◽  
Water Resource Security ◽  
Resource Security

AbstractThis paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning.

Application of ANN Back-Propagation for Fracture Design Parameters in Extra-Low Cycle Rotating Bending Fatigue

2009 ◽  
Vol 610-613 ◽  
pp. 450-453
Author(s):  
Hong Yan Duan ◽  
You Tang Li ◽  
Jin Zhang ◽  
Gui Ping He
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Back Propagation ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Bending Fatigue ◽  
The Neural Network ◽  
Rotating Bending Fatigue ◽  
Rotating Bending

The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth, the presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

scholarly journals Back-propagation neural network model to predict visibility at a road link-level

2020 ◽  
Vol 8 ◽  
pp. 100250
Author(s):  
Venkata R. Duddu ◽  
Srinivas S. Pulugurtha ◽  
Ajinkya S. Mane ◽  
Christopher Godfrey
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network

scholarly journals Computational Fluid Dynamic Design and Experimental Study of a Temperature Sensor Array Used in Climate Reference Station

2019 ◽  
Vol 36 (9) ◽  
pp. 1835-1847
Author(s):  
Jie Yang ◽  
Qingquan Liu ◽  
Wei Dai
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Air Temperature ◽  
Neural Network Model ◽  
Temperature Sensor ◽  
Sensor Array ◽  
The Neural Network ◽  
Temperature Observation ◽  
The Mean ◽  
Cfd Method

Accurate air temperature measurements are demanded for climate change research. However, air temperature sensors installed in a screen or a radiation shield have traditionally resisted observation accuracy due to a number of factors, particularly solar radiation. Here we present a novel temperature sensor array to improve the air temperature observation accuracy. To obtain an optimum design of the sensor array, we perform a series of analyses of the sensor array with various structures based on a computational fluid dynamics (CFD) method. Then the CFD method is applied to obtain quantitative radiation errors of the optimum temperature sensor array. For further improving the measurement accuracy of the sensor array, an artificial neural network model is developed to learn the relationship between the radiation error and environment variables. To assess the extent to which the actual performance adheres to the theoretical CFD model and the neural network model, air temperature observation experiments are conducted. An aspirated temperature measurement platform with a forced airflow rate up to 20 m s−1 served as an air temperature reference. The average radiation errors of a temperature sensor equipped with a naturally ventilated radiation shield and a temperature sensor installed in a screen are 0.42° and 0.23°C, respectively. By contrast, the mean radiation error of the temperature sensor array is approximately 0.03°C. The mean absolute error (MAE) between the radiation errors provided by the experiments and the radiation errors given by the neural network model is 0.007°C, and the root-mean-square error (RMSE) is 0.009°C.

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