The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical r...The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.展开更多
Precise and accurate rainfall simulation is essential for Tanzania, where complex topography and diverse climatic influences result in variable precipitation patterns. In this study, the 31st October 2023 to 02nd Nove...Precise and accurate rainfall simulation is essential for Tanzania, where complex topography and diverse climatic influences result in variable precipitation patterns. In this study, the 31st October 2023 to 02nd November 2023 daily observation rainfall was used to assess the performance of 5 land surface models (LSMs) and 7 microphysics schemes (MPs) using the Weather Research and Forecasting (WRF) model. The 35 different simulations were then evaluated using the observation data from the ground stations (OBS) and the gridded satellite (CHIRPS) dataset. It was found that the WSM6 scheme performed better than other MPs even though the performance of the LSMs was dependent on the observation data used. The CLM4 performed better than others when the simulations were compared with OBS whereas the 5 Layer Slab produced the lowest mean absolute error (MAE) and root mean square error (RMSE) values while the Noah-MP and RUC schemes produced the lowest average values of RMSE and MAE respectively when the CHIRPS dataset was used. The difference in performance of land surface models when compared to different sets of observation data was attributed to the fact that each observation dataset had a different number of points over the same area, influencing their performances. Furthermore, it was revealed that the CLM4-WSM6 combination performed better than others in the simulation of this event when it was compared against OBS while the 5 Layer Slab-WSM6 combination performed well when the CHIRPS dataset was used for comparison. This research highlights the critical role of the selection of land surface models and microphysics schemes in forecasting extreme rainfall events and underscores the importance of integrating different observational data for model validation. These findings contribute to improving predictive capabilities for extreme rainfall events in similar climatic regions.展开更多
Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the perform...Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the performance of traditional linear time series models, namely Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, and Moving Average (MA) against neural network architectures. The primary goal is to evaluate the effectiveness of these models in predicting healthcare outcomes using patient records, specifically the Cancerpatient.xlsx dataset, which tracks variables such as patient age, symptoms, genetic risk factors, and environmental exposures over time. The proposed strategy involves training each model on historical patient data to predict age progression and other related health indicators, with performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Our findings reveal that neural networks consistently outperform ARIMA and SARIMA by capturing non-linear patterns and complex temporal dependencies within the dataset, resulting in lower forecasting errors. This research highlights the potential of neural networks to enhance predictive accuracy in healthcare applications, supporting better resource allocation, patient monitoring, and long-term health outcome predictions.展开更多
In today’s rapidly evolving business environment,enterprises face unprecedented competitive pressures and complexities,necessitating efficient and precise strategic decision-making capabilities.Management accounting,...In today’s rapidly evolving business environment,enterprises face unprecedented competitive pressures and complexities,necessitating efficient and precise strategic decision-making capabilities.Management accounting,as the core of internal corporate management,plays a critical role in optimizing resource allocation,long-term planning,and formulating market competition strategies.This paper explores the application of Artificial Intelligence(AI)in management accounting,aiming to analyze the current state of AI in management accounting,examine its role in supporting external strategic decisions,and develop an AI-driven strategic forecasting and analysis model.The findings indicate that AI technology,through its advanced data processing and analytical capabilities,significantly enhances the efficiency and accuracy of management accounting,optimizes internal resource allocation,and strengthens enterprises’market competitiveness.展开更多
Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient...Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.展开更多
A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale ...A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor pro files extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities （including downdraft） derived from the WRF model, the new WRF-driven 1DSC modeling system （WRF-1DSC） was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to repro duce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional short range forecasting system, This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.展开更多
For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model f...For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.展开更多
In order to extend the forecasting period of flood and improve the accuracy of flood forecasting,this paper took Bailian River Reservoir which located in Huanggang City of Hubei Province as an example and carried out ...In order to extend the forecasting period of flood and improve the accuracy of flood forecasting,this paper took Bailian River Reservoir which located in Huanggang City of Hubei Province as an example and carried out basin flood simulation and forecasting by coupling the quantitative precipitation forecasting products of numerical forecast operation model of Institute of Heavy Rain in Wuhan(WRF)and the European Center for Medium-range Weather Forecasts(ECMWF)with the three water sources Xin an River model.The experimental results showed that the spatiotemporal distribution of rainfall predicted by EC is closer to the actual situation compared to WRF;the efficiency coefficient and peak time difference of EC used for flood forecasting are comparable to WRF,but the average relative error of flood peaks is about 14%smaller than WRF.Overall,the precipitation forecasting products of the two numerical models can be used for flood forecasting in the Bailian River basin.Some forecasting indicators have certain reference value,and there is still significant room for improvement in the forecasting effects of the two models.展开更多
Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world ove...Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world over years has continued to make food and non-alcoholic beverages not to be accessible and affordable to individuals and families having a low income. The aim of this particular research study was to identify how Kenya’s CPI of food and non-alcoholic beverages could be modelled using Autoregressive Integrated Moving Average (ARIMA) models for forecasting future values for the next two years. The data used for the study was that of Kenya’s CPI of food and non-alcoholic beverages for the period starting from February 2009 to April 2024 obtained from the International Monetary Fund (IMF) database. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) and assessing whether residuals of the model were independent and normally distributed with a variance that is constant an whether the model has most of its coefficients being significant statistically. ARIMA (3, 1, 0) (1, 0, 0) model was identified as the best ARIMA model for modeling Kenya’s CPI of food and non-beverages for forecasting future values among the ARIMA models considered. Using this particular model, Kenya’s CPI of food and non-alcoholic beverages was forecasted to increase only slightly with time to reach a value of about 165.70 by March 2026.展开更多
Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation proced...Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.展开更多
Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN....Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.展开更多
[Objective] The aim was to establish drought forecasting model with high precision. [Method] With an ARIMA regression model, the research performed Palmer Drought mode（PDSI） time series modeling analysis of Henan Pr...[Objective] The aim was to establish drought forecasting model with high precision. [Method] With an ARIMA regression model, the research performed Palmer Drought mode（PDSI） time series modeling analysis of Henan Province based on PDSI time series and DPS（Data Processing Software） in order to build drought forecasting model. [Result] It is feasible to perform drought forecasting with appropriate parameters. [Conclusion] ARIMA model is practical and more precise in PDSI-based drought analysis and forecasting.展开更多
The subset threshold auto regressive (SSTAR) model, which is capable of reproducing the limit cycle behavior of nonlinear time series, is introduced. The algorithm for fitting the sampled data with SSTAR model is pr...The subset threshold auto regressive (SSTAR) model, which is capable of reproducing the limit cycle behavior of nonlinear time series, is introduced. The algorithm for fitting the sampled data with SSTAR model is proposed and applied to model and forecast power load. Numerical example verifies that desirable accuracy of short term load forecasting can be achieved by using the SSTAR model.展开更多
The airflow and dispersion of a pollutant in a complex urban area of Beijing, China, were numerically examined by coupling a Computational Fluid Dynamics （CFD） model with a mesoscale weather model. The models used w...The airflow and dispersion of a pollutant in a complex urban area of Beijing, China, were numerically examined by coupling a Computational Fluid Dynamics （CFD） model with a mesoscale weather model. The models used were Open Source Field Operation and Manipulation （OpenFOAM） software package and Weather Research and Forecasting （WRF） model. OpenFOAM was firstly validated against wind-tunnel experiment data. Then, the WRF model was integrated for 42 h starting from 0800 LST 08 September 2009, and the coupled model was used to compute the flow fields at 1000 LST and 1400 LST 09 September 2009. During the WRF-simulated period, a high pressure system was dominant over the Beijing area. The WRF-simulated local circulations were characterized by mountain valley winds, which matched well with observations. Results from the coupled model simulation demonstrated that the airflows around actual buildings were quite different from the ambient wind on the boundary provided by the WRF model, and the pollutant dispersion pattern was complicated under the influence of buildings. A higher concentration level of the pollutant near the surface was found in both the step-down and step-up notches, but the reason for this higher level in each configurations was different： in the former, it was caused by weaker vertical flow, while in the latter it was caused by a downward-shifted vortex. Overall, the results of this study suggest that the coupled WRF-OpenFOAM model is an important tool that can be used for studying and predicting urban flow and dispersions in densely built-up areas.展开更多
文摘The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.
文摘Precise and accurate rainfall simulation is essential for Tanzania, where complex topography and diverse climatic influences result in variable precipitation patterns. In this study, the 31st October 2023 to 02nd November 2023 daily observation rainfall was used to assess the performance of 5 land surface models (LSMs) and 7 microphysics schemes (MPs) using the Weather Research and Forecasting (WRF) model. The 35 different simulations were then evaluated using the observation data from the ground stations (OBS) and the gridded satellite (CHIRPS) dataset. It was found that the WSM6 scheme performed better than other MPs even though the performance of the LSMs was dependent on the observation data used. The CLM4 performed better than others when the simulations were compared with OBS whereas the 5 Layer Slab produced the lowest mean absolute error (MAE) and root mean square error (RMSE) values while the Noah-MP and RUC schemes produced the lowest average values of RMSE and MAE respectively when the CHIRPS dataset was used. The difference in performance of land surface models when compared to different sets of observation data was attributed to the fact that each observation dataset had a different number of points over the same area, influencing their performances. Furthermore, it was revealed that the CLM4-WSM6 combination performed better than others in the simulation of this event when it was compared against OBS while the 5 Layer Slab-WSM6 combination performed well when the CHIRPS dataset was used for comparison. This research highlights the critical role of the selection of land surface models and microphysics schemes in forecasting extreme rainfall events and underscores the importance of integrating different observational data for model validation. These findings contribute to improving predictive capabilities for extreme rainfall events in similar climatic regions.
文摘Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the performance of traditional linear time series models, namely Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, and Moving Average (MA) against neural network architectures. The primary goal is to evaluate the effectiveness of these models in predicting healthcare outcomes using patient records, specifically the Cancerpatient.xlsx dataset, which tracks variables such as patient age, symptoms, genetic risk factors, and environmental exposures over time. The proposed strategy involves training each model on historical patient data to predict age progression and other related health indicators, with performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Our findings reveal that neural networks consistently outperform ARIMA and SARIMA by capturing non-linear patterns and complex temporal dependencies within the dataset, resulting in lower forecasting errors. This research highlights the potential of neural networks to enhance predictive accuracy in healthcare applications, supporting better resource allocation, patient monitoring, and long-term health outcome predictions.
文摘In today’s rapidly evolving business environment,enterprises face unprecedented competitive pressures and complexities,necessitating efficient and precise strategic decision-making capabilities.Management accounting,as the core of internal corporate management,plays a critical role in optimizing resource allocation,long-term planning,and formulating market competition strategies.This paper explores the application of Artificial Intelligence(AI)in management accounting,aiming to analyze the current state of AI in management accounting,examine its role in supporting external strategic decisions,and develop an AI-driven strategic forecasting and analysis model.The findings indicate that AI technology,through its advanced data processing and analytical capabilities,significantly enhances the efficiency and accuracy of management accounting,optimizes internal resource allocation,and strengthens enterprises’market competitiveness.
基金supported by the Natural Science Foundation of China(Grant Nos.42088101 and 42205149)Zhongwang WEI was supported by the Natural Science Foundation of China(Grant No.42075158)+1 种基金Wei SHANGGUAN was supported by the Natural Science Foundation of China(Grant No.41975122)Yonggen ZHANG was supported by the National Natural Science Foundation of Tianjin(Grant No.20JCQNJC01660).
文摘Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.
基金jointly supported by the Main Direction Program of Knowledge Innovation of the Chinese Academy of Sciences(Grant No.KZCX2EW203)the National Key Basic Research Program of China(Grant No.2013CB430105)the National Department of Public Benefit Research Foundation(Grant No.GYHY201006031)
文摘A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor pro files extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities （including downdraft） derived from the WRF model, the new WRF-driven 1DSC modeling system （WRF-1DSC） was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to repro duce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional short range forecasting system, This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.
文摘For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.
基金Supported by Open Project Fund of China Meteorological Administration Basin Heavy Rainfall Key Laboratory(2023BHR-Y26)Innovation Project Fund of Wuhan Metropolitan Area Meteorological Joint Science and Technology(WHCSQY202305)+1 种基金Innovation and Development Special Project of China Meteorological Administration(CXFZ2022J019)Project of Huanggang Meteorological Bureau's Scientific Research(2022Y02).
文摘In order to extend the forecasting period of flood and improve the accuracy of flood forecasting,this paper took Bailian River Reservoir which located in Huanggang City of Hubei Province as an example and carried out basin flood simulation and forecasting by coupling the quantitative precipitation forecasting products of numerical forecast operation model of Institute of Heavy Rain in Wuhan(WRF)and the European Center for Medium-range Weather Forecasts(ECMWF)with the three water sources Xin an River model.The experimental results showed that the spatiotemporal distribution of rainfall predicted by EC is closer to the actual situation compared to WRF;the efficiency coefficient and peak time difference of EC used for flood forecasting are comparable to WRF,but the average relative error of flood peaks is about 14%smaller than WRF.Overall,the precipitation forecasting products of the two numerical models can be used for flood forecasting in the Bailian River basin.Some forecasting indicators have certain reference value,and there is still significant room for improvement in the forecasting effects of the two models.
文摘Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world over years has continued to make food and non-alcoholic beverages not to be accessible and affordable to individuals and families having a low income. The aim of this particular research study was to identify how Kenya’s CPI of food and non-alcoholic beverages could be modelled using Autoregressive Integrated Moving Average (ARIMA) models for forecasting future values for the next two years. The data used for the study was that of Kenya’s CPI of food and non-alcoholic beverages for the period starting from February 2009 to April 2024 obtained from the International Monetary Fund (IMF) database. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) and assessing whether residuals of the model were independent and normally distributed with a variance that is constant an whether the model has most of its coefficients being significant statistically. ARIMA (3, 1, 0) (1, 0, 0) model was identified as the best ARIMA model for modeling Kenya’s CPI of food and non-beverages for forecasting future values among the ARIMA models considered. Using this particular model, Kenya’s CPI of food and non-alcoholic beverages was forecasted to increase only slightly with time to reach a value of about 165.70 by March 2026.
文摘Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.
基金The National Natural Science Foundation of China(No.50479017).
文摘Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.
文摘[Objective] The aim was to establish drought forecasting model with high precision. [Method] With an ARIMA regression model, the research performed Palmer Drought mode（PDSI） time series modeling analysis of Henan Province based on PDSI time series and DPS（Data Processing Software） in order to build drought forecasting model. [Result] It is feasible to perform drought forecasting with appropriate parameters. [Conclusion] ARIMA model is practical and more precise in PDSI-based drought analysis and forecasting.
文摘The subset threshold auto regressive (SSTAR) model, which is capable of reproducing the limit cycle behavior of nonlinear time series, is introduced. The algorithm for fitting the sampled data with SSTAR model is proposed and applied to model and forecast power load. Numerical example verifies that desirable accuracy of short term load forecasting can be achieved by using the SSTAR model.
基金supported by the Public Welfare Special Fund Program(Meteorology)of the Chinese Ministry of Finance under Grant No.GYHY201106033
文摘The airflow and dispersion of a pollutant in a complex urban area of Beijing, China, were numerically examined by coupling a Computational Fluid Dynamics （CFD） model with a mesoscale weather model. The models used were Open Source Field Operation and Manipulation （OpenFOAM） software package and Weather Research and Forecasting （WRF） model. OpenFOAM was firstly validated against wind-tunnel experiment data. Then, the WRF model was integrated for 42 h starting from 0800 LST 08 September 2009, and the coupled model was used to compute the flow fields at 1000 LST and 1400 LST 09 September 2009. During the WRF-simulated period, a high pressure system was dominant over the Beijing area. The WRF-simulated local circulations were characterized by mountain valley winds, which matched well with observations. Results from the coupled model simulation demonstrated that the airflows around actual buildings were quite different from the ambient wind on the boundary provided by the WRF model, and the pollutant dispersion pattern was complicated under the influence of buildings. A higher concentration level of the pollutant near the surface was found in both the step-down and step-up notches, but the reason for this higher level in each configurations was different： in the former, it was caused by weaker vertical flow, while in the latter it was caused by a downward-shifted vortex. Overall, the results of this study suggest that the coupled WRF-OpenFOAM model is an important tool that can be used for studying and predicting urban flow and dispersions in densely built-up areas.
