To investigate the situation of conventional oil and gas, this paper examines the global oil and gas discoveries, proved reserves, production, consumption and price. All the influencing factors can be subjected to ris...To investigate the situation of conventional oil and gas, this paper examines the global oil and gas discoveries, proved reserves, production, consumption and price. All the influencing factors can be subjected to risk and opportunity analyses, so in the paper, we build upon a risk-opportunity analysis framework, which is a new train of thought. To forecast the peak time of oil and gas production, we used the methods of multi-Hubbert model forecasting and data forecasting. Our results showed that the world oil production will reach a peak between 2010 and 2015 and the gas production will reach a peak around 2030 Oil peak is coming and gas peak is on the way. The main purpose of forecasting oil and gas production peak is give people enough time for preparing mitigation and adaptation plans. This means taking decisive action well before the problem is obvious.展开更多
Based on the fire and meteorological data of forest and grassland in Inner Mongolia in recent 30 years,a forest and grassland fire risk grade forecast model is established,and a refined forest and grassland fire risk ...Based on the fire and meteorological data of forest and grassland in Inner Mongolia in recent 30 years,a forest and grassland fire risk grade forecast model is established,and a refined forest and grassland fire risk level forecast system based on smart grid is developed. The results show that predictors are determined about forest and grassland fire risk grade,such as precipitation,minimum relative humidity,maximum temperature,maximum wind speed,number of sunny or rainy days,and forest and grassland combustible stock. According to fire risk division conclusion,forest and grassland areas are divided into 5 forecast areas. By using discriminant analysis and weighted factor overlay method,an elaborate fire risk grade forecast model is established in different forecast areas of Inner Mongolia forest and grassland. By using smart grid forecast field data,an elaborate fire risk grade forecasting system is established for making fire risk grade forecast during 24,48 and 72 h.展开更多
In this study,an operational forecasting system of sea dike risk in the southern Zhejiang Province,South China was developed based on a coupled storm-surge and wave model.This forecasting system is important because o...In this study,an operational forecasting system of sea dike risk in the southern Zhejiang Province,South China was developed based on a coupled storm-surge and wave model.This forecasting system is important because of the high cost of storm-surge damage and the need for rapid emergency planning.A comparison with astronomical tides in 2016 and the validation of storm surges and high water marks of 20 typhoons verified that the forecast system has a good simulation ability.The system can forecast relatively realistic water levels and wave heights as shown under the parametric atmospheric forces simulated in a case study;the sea dikes in credible high risk were mainly located in the estuaries,rivers,and around the islands in the southern Zhejiang.Therefore,the forecast system is applicable in the southern Zhejiang with a support to the effective prevention from typhoon storm-surge damage.展开更多
Volatility is an important variable in the financial market. We propose a model-free implied volatility method to measure the volatility and test the volatility risk premium. The model-free implied volatility does not...Volatility is an important variable in the financial market. We propose a model-free implied volatility method to measure the volatility and test the volatility risk premium. The model-free implied volatility does not depend on the option pricing model, and extracts information from all the option contracts. We provide empirical evidence from the S & P 500 index option that model-free implied volatility is more accurate to forecast the future volatility and the volatility risk premium does not exist.展开更多
Investigation of spatial distribution of oil and gas resource and accurate prediction of the geographic location of its undiscovered resource is significant for reducing exploration risk and improving exploration bene...Investigation of spatial distribution of oil and gas resource and accurate prediction of the geographic location of its undiscovered resource is significant for reducing exploration risk and improving exploration benefit.A new method for predicting spatial distribution of oil resource is discussed in this paper.It consists of prediction of risk probability in petroleum exploration and simulation of hydrocarbon abundance. Exploration risk probability is predicted by multivariate statistics,fuzzy mathematics and information processing techniques.A spatial attribute database for sample wells was set up and the Mahalanobis distance and Fuzzy value of given samples were obtained.Then,the Bayesian formula was used to calculate the hydrocarbon-bearing probability at the area of exploration wells.Finally,a hydrocarbon probability template is formed and used to forecast the probability of the unknown area. The hydrocarbon abundance is simulated based on Fourier integrals,frequency spectrum synthesis and fractal theory.Firstly,the fast Fourier transformation（FFT） is used to transform the known hydrocarbon abundance from the spatial domain to the frequency domain,then,frequency spectrum synthesis is used to produce the fractal frequency spectrum,and FFT is applied to get the phase information of hydrocarbon-bearing probability.Finally,the frequency spectrum simulation is used to calculate the renewed hydrocarbon abundance in the play. This method is used to predict the abundance and possible locations of the undiscovered petroleum accumulations in the Nanpu Sag of the Bohai Bay Basin,China.The prediction results for the well-explored onshore area of the northern Nanpu Sag agree well with the actual situations.For the less-explored offshore areas in the southern Nanpu Sag,the prediction results suggest high hydrocarbon abundance in Nanpu-1 and Nanpu-2,providing a useful guiding for future exploration.展开更多
The objective of the research is to analyze the ability of the artificial neural network model developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this ...The objective of the research is to analyze the ability of the artificial neural network model developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this paper introduces a litera-ture review on the application of artificial intelligence systems for credit risk management. In an empirical point of view, this research compares the architecture of the artificial neural network model developed in this research to an-other one, built for a research conducted in 2004 with a similar panel of companies, showing the differences between the two neural network models.展开更多
The risk situation assessment and forecast technique of network security is a basic method of active defense techniques. In order to assess the risk of network security two methods were used to define the index of ris...The risk situation assessment and forecast technique of network security is a basic method of active defense techniques. In order to assess the risk of network security two methods were used to define the index of risk and forecast index in time series, they were analytical hierarchy process （AHP） and support vector regression （SVR）. The module framework applied the methods above was also discussed. Experiment results showed the forecast values were so close to actual values and so it proved the approach is correct.展开更多
The main business of Life Insurers is Long Term contractual obligations with a typical lifetime of 20 - 40 years. Therefore, the Solvency metric is defined by the adequacy of capital to service the cash flow requireme...The main business of Life Insurers is Long Term contractual obligations with a typical lifetime of 20 - 40 years. Therefore, the Solvency metric is defined by the adequacy of capital to service the cash flow requirements arising from the said obligations. The main component inducing volatility in Capital is market sensitive Assets, such as Bonds and Equity. Bond and Equity prices in Sri Lanka are highly sensitive to macro-economic elements such as investor sentiment, political stability, policy environment, economic growth, fiscal stimulus, utility environment and in the case of Equity, societal sentiment on certain companies and industries. Therefore, if an entity is to accurately forecast the impact on solvency through asset valuation, the impact of macro-economic variables on asset pricing must be modelled mathematically. This paper explores mathematical, actuarial and statistical concepts such as Brownian motion, Markov Processes, Derivation and Integration as well as Probability theorems such as the Probability Density Function in determining the optimum mathematical model which depicts the accurate relationship between macro-economic variables and asset pricing.展开更多
In order to solve the problem of low accuracy of construction project duration prediction, this paper proposes a CNN attention BP combination model </span><span style="font-family:"white-space:...In order to solve the problem of low accuracy of construction project duration prediction, this paper proposes a CNN attention BP combination model </span><span style="font-family:"white-space:normal;">project risk prediction model based on attention mechanism, one-dimensional </span><span style="font-family:"white-space:normal;">convolutional neural network (1d-cnn) and BP neural network. Firstly, the literature analysis method is used to select the risk evaluation index value of construction project, and the attention mechanism is used to determine the weight of risk factors on construction period prediction;then, BP neural network is used to predict the project duration, and accuracy, cross entropy loss function and F1 score are selected to comprehensively evaluate the performance of 1d-cnn-attention-bp combined model. The experimental results show that the duration risk prediction accuracy of the risk prediction model proposed in this paper is more than 90%, which can meet the risk prediction of construction projects with high accuracy.展开更多
文摘To investigate the situation of conventional oil and gas, this paper examines the global oil and gas discoveries, proved reserves, production, consumption and price. All the influencing factors can be subjected to risk and opportunity analyses, so in the paper, we build upon a risk-opportunity analysis framework, which is a new train of thought. To forecast the peak time of oil and gas production, we used the methods of multi-Hubbert model forecasting and data forecasting. Our results showed that the world oil production will reach a peak between 2010 and 2015 and the gas production will reach a peak around 2030 Oil peak is coming and gas peak is on the way. The main purpose of forecasting oil and gas production peak is give people enough time for preparing mitigation and adaptation plans. This means taking decisive action well before the problem is obvious.
基金Supported by Scientific and Technological Project of Inner Mongolia Autonomous Region (2020GG0016)。
文摘Based on the fire and meteorological data of forest and grassland in Inner Mongolia in recent 30 years,a forest and grassland fire risk grade forecast model is established,and a refined forest and grassland fire risk level forecast system based on smart grid is developed. The results show that predictors are determined about forest and grassland fire risk grade,such as precipitation,minimum relative humidity,maximum temperature,maximum wind speed,number of sunny or rainy days,and forest and grassland combustible stock. According to fire risk division conclusion,forest and grassland areas are divided into 5 forecast areas. By using discriminant analysis and weighted factor overlay method,an elaborate fire risk grade forecast model is established in different forecast areas of Inner Mongolia forest and grassland. By using smart grid forecast field data,an elaborate fire risk grade forecasting system is established for making fire risk grade forecast during 24,48 and 72 h.
基金Supported by the National Key Research and Development Program of China(No.2016YFC1402000)
文摘In this study,an operational forecasting system of sea dike risk in the southern Zhejiang Province,South China was developed based on a coupled storm-surge and wave model.This forecasting system is important because of the high cost of storm-surge damage and the need for rapid emergency planning.A comparison with astronomical tides in 2016 and the validation of storm surges and high water marks of 20 typhoons verified that the forecast system has a good simulation ability.The system can forecast relatively realistic water levels and wave heights as shown under the parametric atmospheric forces simulated in a case study;the sea dikes in credible high risk were mainly located in the estuaries,rivers,and around the islands in the southern Zhejiang.Therefore,the forecast system is applicable in the southern Zhejiang with a support to the effective prevention from typhoon storm-surge damage.
文摘Volatility is an important variable in the financial market. We propose a model-free implied volatility method to measure the volatility and test the volatility risk premium. The model-free implied volatility does not depend on the option pricing model, and extracts information from all the option contracts. We provide empirical evidence from the S & P 500 index option that model-free implied volatility is more accurate to forecast the future volatility and the volatility risk premium does not exist.
文摘Investigation of spatial distribution of oil and gas resource and accurate prediction of the geographic location of its undiscovered resource is significant for reducing exploration risk and improving exploration benefit.A new method for predicting spatial distribution of oil resource is discussed in this paper.It consists of prediction of risk probability in petroleum exploration and simulation of hydrocarbon abundance. Exploration risk probability is predicted by multivariate statistics,fuzzy mathematics and information processing techniques.A spatial attribute database for sample wells was set up and the Mahalanobis distance and Fuzzy value of given samples were obtained.Then,the Bayesian formula was used to calculate the hydrocarbon-bearing probability at the area of exploration wells.Finally,a hydrocarbon probability template is formed and used to forecast the probability of the unknown area. The hydrocarbon abundance is simulated based on Fourier integrals,frequency spectrum synthesis and fractal theory.Firstly,the fast Fourier transformation（FFT） is used to transform the known hydrocarbon abundance from the spatial domain to the frequency domain,then,frequency spectrum synthesis is used to produce the fractal frequency spectrum,and FFT is applied to get the phase information of hydrocarbon-bearing probability.Finally,the frequency spectrum simulation is used to calculate the renewed hydrocarbon abundance in the play. This method is used to predict the abundance and possible locations of the undiscovered petroleum accumulations in the Nanpu Sag of the Bohai Bay Basin,China.The prediction results for the well-explored onshore area of the northern Nanpu Sag agree well with the actual situations.For the less-explored offshore areas in the southern Nanpu Sag,the prediction results suggest high hydrocarbon abundance in Nanpu-1 and Nanpu-2,providing a useful guiding for future exploration.
文摘The objective of the research is to analyze the ability of the artificial neural network model developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this paper introduces a litera-ture review on the application of artificial intelligence systems for credit risk management. In an empirical point of view, this research compares the architecture of the artificial neural network model developed in this research to an-other one, built for a research conducted in 2004 with a similar panel of companies, showing the differences between the two neural network models.
基金Supported bythe Basic Research of Commission ofScience , Technology and Industry for National Defense (03058720)
文摘The risk situation assessment and forecast technique of network security is a basic method of active defense techniques. In order to assess the risk of network security two methods were used to define the index of risk and forecast index in time series, they were analytical hierarchy process （AHP） and support vector regression （SVR）. The module framework applied the methods above was also discussed. Experiment results showed the forecast values were so close to actual values and so it proved the approach is correct.
文摘The main business of Life Insurers is Long Term contractual obligations with a typical lifetime of 20 - 40 years. Therefore, the Solvency metric is defined by the adequacy of capital to service the cash flow requirements arising from the said obligations. The main component inducing volatility in Capital is market sensitive Assets, such as Bonds and Equity. Bond and Equity prices in Sri Lanka are highly sensitive to macro-economic elements such as investor sentiment, political stability, policy environment, economic growth, fiscal stimulus, utility environment and in the case of Equity, societal sentiment on certain companies and industries. Therefore, if an entity is to accurately forecast the impact on solvency through asset valuation, the impact of macro-economic variables on asset pricing must be modelled mathematically. This paper explores mathematical, actuarial and statistical concepts such as Brownian motion, Markov Processes, Derivation and Integration as well as Probability theorems such as the Probability Density Function in determining the optimum mathematical model which depicts the accurate relationship between macro-economic variables and asset pricing.
文摘In order to solve the problem of low accuracy of construction project duration prediction, this paper proposes a CNN attention BP combination model </span><span style="font-family:"white-space:normal;">project risk prediction model based on attention mechanism, one-dimensional </span><span style="font-family:"white-space:normal;">convolutional neural network (1d-cnn) and BP neural network. Firstly, the literature analysis method is used to select the risk evaluation index value of construction project, and the attention mechanism is used to determine the weight of risk factors on construction period prediction;then, BP neural network is used to predict the project duration, and accuracy, cross entropy loss function and F1 score are selected to comprehensively evaluate the performance of 1d-cnn-attention-bp combined model. The experimental results show that the duration risk prediction accuracy of the risk prediction model proposed in this paper is more than 90%, which can meet the risk prediction of construction projects with high accuracy.
