scholarly journals A Novel Integrated Learning Model for Rainfall Prediction CEEMD- FCMSE -Stacking

2022 ◽  
Author(s):  
Xianqi Zhang ◽  
Kai Wang ◽  
Tao Wang
Keyword(s):  
Ensemble Learning ◽  
Goodness Of Fit ◽  
Prediction Method ◽  
Absolute Error ◽  
Prediction Performance ◽  
Monthly Precipitation ◽  
Spatial And Temporal Patterns ◽  
Rainfall Prediction ◽  
Multi Scale ◽  
Precipitation Changes

Abstract Scientific prediction of precipitation changes has important guiding value and significance for revealing regional spatial and temporal patterns of precipitation changes, flood climate prediction, etc. Based on the fact that CEEMD can effectively overcome the interference of modal aliasing and white noise, fine composite multi-scale entropy can reorganize the same FCMSE value to reduce the modal component and improve the computational efficiency, and Stacking ensemble learning can effectively and conveniently improve the fitting effect of machine learning, a rainfall prediction method based on CEEMD-fine composite multi-scale entropy and Stacking ensemble learning is constructed, and it is applied to the prediction of monthly precipitation in the Xixia. The results show that, under the same conditions, the CEEMD-RCMSE-Stacking model reduces the root mean square error by 83.48% and 62.08%, and the mean absolute error by 83.25% and 61.84%, respectively, compared with the single Stacking model and CEEMD-LSTM, while the goodness-of-fit coefficients improve by 15.94% and 2.34%, respectively, which means that the CEEMD-RCMSE-Stacking model has higher prediction performance. The CEEMD-RCMSE-Stacking model has higher prediction performance.

scholarly journals An interpretable prediction method for university student academic crisis warning

2021 ◽  
Author(s):  
Zhai Mingyu ◽  
Wang Sutong ◽  
Wang Yanzhang ◽  
Wang Dujuan
Keyword(s):  
Academic Achievement ◽  
Visual Analysis ◽  
Prediction Method ◽  
Absolute Error ◽  
University Student ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Achievement Prediction ◽  
Academic Achievement Prediction ◽  
Education Administrators

AbstractData-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination ($$R^{2}$$ R 2 ) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

Machine learning-based prediction of component self-alignment in vapour phase and infrared soldering

2019 ◽  
Vol 31 (3) ◽  
pp. 163-168 ◽  
Author(s):  
Oliver Krammer ◽  
Péter Martinek ◽  
Balazs Illes ◽  
László Jakab
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Prediction Method ◽  
Absolute Error ◽  
Vapour Phase ◽  
The Self ◽  
Phase Method ◽  
Content Type ◽  
Artificial Neural

Purpose This paper aims to investigate the self-alignment of 0603 size (1.5 × 0.75 mm) chip resistors, which were soldered by infrared or vapour phase soldering. The results were used for establishing an artificial neural network for predicting the component movement during the soldering. Design/methodology/approach The components were soldered onto an FR4 testboard, which was designed to facilitate the measuring of the position of the components both prior to and after the soldering. A semi-automatic placement machine misplaced the components intentionally, and the self-alignment ability was determined for soldering techniques of both infrared and vapour phase soldering. An artificial neural network-based prediction method was established, which is able to predict the position of chip resistors after soldering as a function of component misplacement prior to soldering. Findings The results showed that the component can self-align from farer distances by using vapour phase method, even from relative misplacement of 50 per cent parallel to the shorter side of the component. Components can self-align from a relative misplacement only of 30 per cent by using infrared soldering method. The established artificial neural network can predict the component self-alignment with an approximately 10-20 per cent mean absolute error. Originality/value It was proven that the vapour phase soldering method is more stable from the component’s self-alignment point of view. Furthermore, machine learning-based predictors can be applied in the field of reflow soldering technology, and artificial neural networks can predict the component self-alignment with an appropriately low error.

scholarly journals Precipitation Atlas for Germany (GePrA)

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 737
Author(s):  
Christopher Jung ◽  
Dirk Schindler
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Spline Interpolation ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Small Scale ◽  
Monthly Precipitation ◽  
New Approach ◽  
Very High Spatial Resolution

A new approach for modeling daily precipitation (RR) at very high spatial resolution (25 m × 25 m) was introduced. It was used to develop the Precipitation Atlas for Germany (GePrA). GePrA is based on 2357 RR time series measured in the period 1981–2018. It provides monthly percentiles (p) of the large-scale RR patterns which were mapped by a thin plate spline interpolation (TPS). A least-squares boosting (LSBoost) approach and orographic predictor variables (PV) were applied to integrate the small-scale precipitation variability in GePrA. Then, a Weibull distribution (Wei) was fitted to RRp. It was found that the mean monthly sum of RR ( R R ¯ s u m ) is highest in July (84 mm) and lowest in April (49 mm). A great dependency of RR on the elevation (ε) was found and quantified. Model validation at 425 stations showed a mean coefficient of determination (R2) of 0.80 and a mean absolute error (MAE) of less than 10 mm in all months. The high spatial resolution, including the effects of the local orography, make GePrA a valuable tool for various applications. Since GePrA does not only describe R R ¯ s u m , but also the entire monthly precipitation distributions, the results of this study enable the seasonal differentiation between dry and wet period at small scales.

scholarly journals Regional Precipitation Model Based on Geographically and Temporally Weighted Regression Kriging

2020 ◽  
Vol 12 (16) ◽  
pp. 2547 ◽  
Author(s):  
Wei Zhang ◽  
Dan Liu ◽  
Shengjie Zheng ◽  
Shuya Liu ◽  
Hugo A. Loáiciga ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Absolute Error ◽  
Weighted Regression ◽  
Distribution Model ◽  
Monthly Precipitation ◽  
Regression Kriging ◽  
Time Step ◽  
Predictive Skill ◽  
Precipitation Model ◽  
Meteorological Modeling

High-resolution precipitation field has been widely used in hydrological and meteorological modeling. This paper establishes the spatial and temporal distribution model of precipitation in Hubei Province from 2006 through 2014, based on the data of 75 meteorological stations. This paper applies a geographically and temporally weighted regression kriging (GTWRK) model to precipitation and assesses the effects of timescales and a time-weighted function on precipitation interpolation. This work’s results indicate that: (1) the optimal timescale of the geographically and temporally weighted regression (GTWR) precipitation model is daily. The fitting accuracy is improved when the timescale is converted from months and years to days. The average mean absolute error (MAE), mean relative error (MRE), and the root mean square error (RMSE) decrease with scaling from monthly to daily time steps by 36%, 56%, and 35%, respectively, and the same statistical indexes decrease by 13%, 15%, and 14%, respectively, when scaling from annual to daily steps; (2) the time weight function based on an exponential function improves the predictive skill of the GTWR model by 3% when compared to geographically weighted regression (GWR) using a monthly time step; and (3) the GTWRK has the highest accuracy, and improves the MAE, MRE and RMSE by 3%, 10% and 1% with respect to monthly precipitation predictions, respectively, and by 3%, 10% and 5% concerning annual precipitation predictions, respectively, compared with the GWR results.

Optimization and Analysis for Coke Consumption Based on Clustering

2011 ◽  
Vol 287-290 ◽  
pp. 1112-1115
Author(s):  
Jun Hong Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Clustering Analysis ◽  
Coke Consumption ◽  
Prediction Method ◽  
Absolute Error ◽  
Coke Rate ◽  
The Neural Network ◽  
Fuel Rate

In order to reduce the coke consumption of Blast Furnace（BF），a relevance analysis is carried out for operation parameters and fuel rate of BF，and a prediction method that is combining clustering analysis and artificial neural network for coke rate is proposed. The data cluster is divided into several classes by clustering analysis，the data similarity is high，and the neural network model is used to realize the prediction of coke rate. By combining the neural network with clustering analysis，the data in one BF is simulated，and the results are compared with the traditional neural network model. The result shows that the improved neural network has a higher accuracy, the average absolute error can be decreased by 3.13kg/t, and the average relative error can be decreased by 5.19%, it will have a good using foreground.

GRU-corr Neural Network Optimized by Improved PSO Algorithm for Time Series Prediction

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040010
Author(s):  
Shao-Pei Ji ◽  
Yu-Long Meng ◽  
Liang Yan ◽  
Gui-Shan Dong ◽  
Dong Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Series Data ◽  
Multi Scale ◽  
Network Parameters ◽  
Angle Data

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method.

scholarly journals VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain

2019 ◽  
Vol 11 (8) ◽  
pp. 2191 ◽  
Author(s):  
Jorge Salas ◽  
Víctor Yepes
Keyword(s):  
Cluster Analysis ◽  
Vulnerability Assessment ◽  
Visual Analytics ◽  
Goodness Of Fit ◽  
Optimization Methods ◽  
Decision Makers ◽  
Decision Making Process ◽  
Multi Scale ◽  
Urban Vulnerability ◽  
And Cluster Analysis

Many-objective optimization methods have proven successful in the integration of research attributes demanded for urban vulnerability assessment models. However, these techniques suffer from the curse of the dimensionality problem, producing an excessive burden in the decision-making process by compelling decision-makers to select alternatives among a large number of candidates. In other fields, this problem has been alleviated through cluster analysis, but there is still a lack in the application of such methods for urban vulnerability assessment purposes. This work addresses this gap by a novel combination of visual analytics and cluster analysis, enabling the decision-maker to select the set of indicators best representing urban vulnerability accordingly to three criteria: expert’s preferences, goodness of fit, and robustness. Based on an assessment framework previously developed, VisualUVAM affords an evaluation of urban vulnerability in Spain at regional, provincial, and municipal scales, whose results demonstrate the effect of the governmental structure of a territory over the vulnerability of the assessed entities.

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