COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be...COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.展开更多
Upper ocean heat content(OHC)has been widely recognized as a crucial precursor to high-impact climate variability,especially for that being indispensable to the long-term memory of the ocean.Assessing the predictabili...Upper ocean heat content(OHC)has been widely recognized as a crucial precursor to high-impact climate variability,especially for that being indispensable to the long-term memory of the ocean.Assessing the predictability of OHC using state-of-the-art climate models is invaluable for improving and advancing climate forecasts.Recently developed retrospective forecast experiments,based on a Community Earth System Model ensemble prediction system,offer a great opportunity to comprehensively explore OHC predictability.Our results indicate that the skill of actual OHC predictions varies across different oceans and diminishes as the lead time of prediction extends.The spatial distribution of the actual prediction skill closely resembles the corresponding persistence skill,indicating that the persistence of OHC serves as the primary predictive signal for its predictability.The decline in actual prediction skill is more pronounced in the Indian and Atlantic oceans than in the Pacific Ocean,particularly within tropical regions.Additionally,notable seasonal variations in the actual prediction skills across different oceans align well with the phase-locking features of OHC variability.The potential predictability of OHC generally surpasses the actual prediction skill at all lead times,highlighting significant room for improvement in current OHC predictions,especially for the North Indian Ocean and the Atlantic Ocean.Achieving such improvements necessitates a collaborative effort to enhance the quality of ocean observations,develop effective data assimilation methods,and reduce model bias.展开更多
Extended range (10-30 d) heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error (NCPE) algorithm that combin...Extended range (10-30 d) heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error (NCPE) algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First, nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process, after which the local change characteristics of the attractors are analyzed. Second, the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5, and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently, without failure, based on the NCPE model; the prediction validity periods for 1-2 d, 3-9 d and 10-30 d are 4, 22 and 74 cases, respectively, without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10-30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability, and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.展开更多
Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and stre...Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy.Conventionally,numerous approaches have been developed in the design of accurate FCP processes.At the same time,classifier efficacy and predictive accuracy are inadequate for real-time applications.In addition,several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets.Thus,there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets.This paper presents a novel multi-vs.optimization(MVO)based feature selection(FS)with an optimal variational auto encoder(OVAE)model for FCP.The proposed multi-vs.optimization based feature selection with optimal variational auto encoder(MVOFS-OVAE)model mainly aims to accomplish forecasting the financial crisis.For achieving this,the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization.In addition,the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach.Followed by,the variational auto encoder(VAE)model is applied for the categorization of financial data into financial crisis or non-financial crisis.Finally,the differential evolution(DE)algorithm is utilized for the parameter tuning of the VAE model.A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.展开更多
The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global...The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.展开更多
During ship operations,frequent heave movements can pose significant challenges to the overall safety of the ship and completion of cargo loading.The existing heave compensation systems suffer from issues such as dead...During ship operations,frequent heave movements can pose significant challenges to the overall safety of the ship and completion of cargo loading.The existing heave compensation systems suffer from issues such as dead zones and control system time lags,which necessitate the development of reasonable prediction models for ship heave movements.In this paper,a novel model based on a time graph convolutional neural network algorithm and particle swarm optimization algorithm(PSO-TGCN)is proposed for the first time to predict the multipoint heave movements of ships under different sea conditions.To enhance the dataset's suitability for training and reduce interference,various filter algorithms are employed to optimize the dataset.The training process utilizes simulated heave data under different sea conditions and measured heave data from multiple points.The results show that the PSO-TGCN model predicts the ship swaying motion in different sea states after 2 s with 84.7%accuracy,while predicting the swaying motion in three different positions.By performing a comparative study,it was also found that the present method achieves better performance that other popular methods.This model can provide technical support for intelligent ship control,improve the control accuracy of intelligent ships,and promote the development of intelligent ships.展开更多
Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is ...Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature.Whenever there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy systems.In this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems.Frequency control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning models.The performance analysis of the proposed method is done with different machine learning based approaches.The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy.Simulation results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.展开更多
Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be i...Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.展开更多
With the integration of global economy development and the rapid growth of science knowledge and technology,the needs of people’s consumption are increasingly personalized and diversified.Such a market background mak...With the integration of global economy development and the rapid growth of science knowledge and technology,the needs of people’s consumption are increasingly personalized and diversified.Such a market background makes sales forecasting become an indispensable part of enterprise management and development.The definition of the sales forecasting is that based on the past few years’sales situation,the enterprises through systematic sales forecasting models estimate of the quantity and amount of all or some specific sales products and services in a specific time in the future.Accurate sales forecasting can promote enterprises to do better in future revenue,and can also encourage enterprises to set and keep an efficient sales management team.This paper will analyze traditional sales forecasting methods and sales forecasting methods based on big data models related to the perspective of machine learning,and then compare them.The research shows that the two sales forecasting methods have their own advantages and disadvantages.In the future,enterprises can adopt the two sales forecasting methods in parallel to maximize the utilization advantage of sales forecasting for enterprises.展开更多
This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neur...This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.展开更多
In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,a...In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.展开更多
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0525.
文摘COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.
基金The National Key R&D Program of China under contract No.2020YFA0608803the Scientific Research Fund of the Second Institute of Oceanography+3 种基金Ministry of Natural Resources under contract No.QNYC2101the National Natural Science Foundation of China under contract No.42105052the Fund of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.SML2021SP310the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.311021001。
文摘Upper ocean heat content(OHC)has been widely recognized as a crucial precursor to high-impact climate variability,especially for that being indispensable to the long-term memory of the ocean.Assessing the predictability of OHC using state-of-the-art climate models is invaluable for improving and advancing climate forecasts.Recently developed retrospective forecast experiments,based on a Community Earth System Model ensemble prediction system,offer a great opportunity to comprehensively explore OHC predictability.Our results indicate that the skill of actual OHC predictions varies across different oceans and diminishes as the lead time of prediction extends.The spatial distribution of the actual prediction skill closely resembles the corresponding persistence skill,indicating that the persistence of OHC serves as the primary predictive signal for its predictability.The decline in actual prediction skill is more pronounced in the Indian and Atlantic oceans than in the Pacific Ocean,particularly within tropical regions.Additionally,notable seasonal variations in the actual prediction skills across different oceans align well with the phase-locking features of OHC variability.The potential predictability of OHC generally surpasses the actual prediction skill at all lead times,highlighting significant room for improvement in current OHC predictions,especially for the North Indian Ocean and the Atlantic Ocean.Achieving such improvements necessitates a collaborative effort to enhance the quality of ocean observations,develop effective data assimilation methods,and reduce model bias.
基金provided by the National Natural Science Foundation of China(Grant Nos.41275039 and 41471305)the Preeminence Youth Cultivation Project of Sichuan (Grant No.2015JQ0037)
文摘Extended range (10-30 d) heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error (NCPE) algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First, nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process, after which the local change characteristics of the attractors are analyzed. Second, the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5, and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently, without failure, based on the NCPE model; the prediction validity periods for 1-2 d, 3-9 d and 10-30 d are 4, 22 and 74 cases, respectively, without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10-30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability, and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.
文摘Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy.Conventionally,numerous approaches have been developed in the design of accurate FCP processes.At the same time,classifier efficacy and predictive accuracy are inadequate for real-time applications.In addition,several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets.Thus,there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets.This paper presents a novel multi-vs.optimization(MVO)based feature selection(FS)with an optimal variational auto encoder(OVAE)model for FCP.The proposed multi-vs.optimization based feature selection with optimal variational auto encoder(MVOFS-OVAE)model mainly aims to accomplish forecasting the financial crisis.For achieving this,the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization.In addition,the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach.Followed by,the variational auto encoder(VAE)model is applied for the categorization of financial data into financial crisis or non-financial crisis.Finally,the differential evolution(DE)algorithm is utilized for the parameter tuning of the VAE model.A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.
基金supported by the State Key Program of the National Natural Science of China(Grant No.41730964)the National Key Research and Development Program on Monitoring,Early Warning and Prevention of Major Natural Disaster(2018YFC1506000)+2 种基金the National Natural Science Foundation of China(Grant Nos.41975091 and 42175047)National Basic Research Program of China(2015CB453203)UK-China Research&Innovation Partnership Fund through the Met Office Climate Science for Service Partnership(CSSP)China as part of the Newton Fund.
文摘The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.
基金financially supported by the National Key Research and Development Program of China (Grant No.2022YFE010700)the National Natural Science Foundation of China (Grant No.52171259)+1 种基金the High-Tech Ship Research Project of Ministry of Industry and Information Technology (Grant No.[2021]342)Foundation of State Key Laboratory of Ocean Engineering in Shanghai Jiao Tong University (Grant No.GKZD010086-2)。
文摘During ship operations,frequent heave movements can pose significant challenges to the overall safety of the ship and completion of cargo loading.The existing heave compensation systems suffer from issues such as dead zones and control system time lags,which necessitate the development of reasonable prediction models for ship heave movements.In this paper,a novel model based on a time graph convolutional neural network algorithm and particle swarm optimization algorithm(PSO-TGCN)is proposed for the first time to predict the multipoint heave movements of ships under different sea conditions.To enhance the dataset's suitability for training and reduce interference,various filter algorithms are employed to optimize the dataset.The training process utilizes simulated heave data under different sea conditions and measured heave data from multiple points.The results show that the PSO-TGCN model predicts the ship swaying motion in different sea states after 2 s with 84.7%accuracy,while predicting the swaying motion in three different positions.By performing a comparative study,it was also found that the present method achieves better performance that other popular methods.This model can provide technical support for intelligent ship control,improve the control accuracy of intelligent ships,and promote the development of intelligent ships.
文摘Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature.Whenever there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy systems.In this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems.Frequency control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning models.The performance analysis of the proposed method is done with different machine learning based approaches.The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy.Simulation results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.
基金Funding Statement:The researchers would like to thank the Deanship of Scientific Research,Qassim University for funding the publication of this project.
文摘Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.
文摘With the integration of global economy development and the rapid growth of science knowledge and technology,the needs of people’s consumption are increasingly personalized and diversified.Such a market background makes sales forecasting become an indispensable part of enterprise management and development.The definition of the sales forecasting is that based on the past few years’sales situation,the enterprises through systematic sales forecasting models estimate of the quantity and amount of all or some specific sales products and services in a specific time in the future.Accurate sales forecasting can promote enterprises to do better in future revenue,and can also encourage enterprises to set and keep an efficient sales management team.This paper will analyze traditional sales forecasting methods and sales forecasting methods based on big data models related to the perspective of machine learning,and then compare them.The research shows that the two sales forecasting methods have their own advantages and disadvantages.In the future,enterprises can adopt the two sales forecasting methods in parallel to maximize the utilization advantage of sales forecasting for enterprises.
文摘This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.
文摘In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.
基金supported by the National Natural Science Foundation of China[grant Nos.42125503 and 42075137]the National Key Research and Development Program of China[grant Nos.2020YFA0608000 and 2020YFA0607900].