Objective:To predict future trends in the incidence of malaria cases in the southeast of Iran as the most important area of malaria using Seasonal Autoregressive Integrated Moving Average(SARIMA)model,and to check the...Objective:To predict future trends in the incidence of malaria cases in the southeast of Iran as the most important area of malaria using Seasonal Autoregressive Integrated Moving Average(SARIMA)model,and to check the effect of meteorological variables on the disease incidence.Methods:SARIMA method was applied to fit a model on malaria incidence from April 2001 to March 2018 in Sistan and Baluchistan province in southeastern Iran.Climatic variables such as temperature,rainfall,rainy days,humidity,sunny hours and wind speed were also included in the multivariable model as covariates.Then,the best fitted model was adopted to predict the number of malaria cases for the next 12 months.Results:The best-fitted univariate model for the prediction of malaria in the southeast of Iran was SARIMA(1,0,0)(1,1,1)12[Akaike Information Criterion(AIC)=307.4,validation root mean square error(RMSE)=0.43].The occurrence of malaria in a given month was mostly related to the number of cases occurring in the previous 1(p=1)and 12(P=1)months.The inverse number of rainy days with 8-month lag(β=0.3292)and temperature with 3-month lag(β=-0.0026)were the best predictors that could improve the predictive performance of the univariate model.Finally,SARIMA(1,0,0)(1,1,1)12 including mean temperature with a 3-month lag(validation RMSE=0.414)was selected as the final multivariable model.Conclusions:The number of malaria cases in a given month can be predicted by the number of cases in the prior 1 and 12 months.The number of rainy days with an 8-month lag and temperature with a 3-month lag can improve the predictive power of the model.展开更多
Objective:To determine the potential effect of environment variables on cutaneous leishmaniasis occurrence using time-series models and compare the predictive ability of seasonal autoregressive integrated moving avera...Objective:To determine the potential effect of environment variables on cutaneous leishmaniasis occurrence using time-series models and compare the predictive ability of seasonal autoregressive integrated moving average(SARIMA)models and Markov switching model(MSM).Methods:This descriptive study employed yearly and monthly data of 49364 parasitologically-confirmed cases of cutaneous leishmaniasis in Isfahan province,located in the center of Iran from January 2000 to December 2019.The data were provided by the leishmaniasis national surveillance system,the meteorological organization of Isfahan province,and Iranian Space Agency for vegetation information.The SARIMA and MSM models were implemented to examine the environmental factors of cutaneous leishmaniasis epidemics.Results:The minimum relative humidity,maximum relative humidity,minimum wind speed,and maximum wind speed were significantly associated with cutaneous leishmaniasis epidemics in different lags(P<0.05).Comparing SARIMA and MSM,Akaikes information criterion(AIC),and mean absolute percentage error(MAPE)in MSM were much smaller than SARIMA models(MSM:AIC=0.95,MAPE=3.5%;SARIMA:AIC=158.93,MAPE:11.45%).Conclusions:SARIMA and MSM can be a useful tool for predicting cutaneous leishmaniasis in Isfahan province.Since cutaneous leishmaniasis falls into one of two states of epidemic and non-epidemic,the use of MSM(dynamic)is recommended,which can provide more information compared to models that use a single distribution for all observations(Box-Jenkins SARIMA model).展开更多
Water is a vital resource.It supports a multitude of industries,civilizations,and agriculture.However,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain i...Water is a vital resource.It supports a multitude of industries,civilizations,and agriculture.However,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain is scarce.Therefore,it is crucial to forecast water demand to provide it to sectors either on regular or emergency days.The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions.This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future.Focusing on the collected data of Jeddah city,Saudi Arabia in the period between 2004 and 2018,we develop a hybrid approach that uses Artificial Neural Networks(ANN)for forecasting and Particle Swarm Optimization algorithm(PSO)for tuning ANNs’hyperparameters.Based on the Root Mean Square Error(RMSE)metric,results show that the(PSO-ANN)is an accurate model for multivariate time series forecasting.Also,the first day is the most difficult day for prediction(highest error rate),while the second day is the easiest to predict(lowest error rate).Finally,correlation analysis shows that the dew point is the most climatic factor affecting water demand.展开更多
Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relie...Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.展开更多
Forecasting environmental parameters in the distant future requires complex modelling and large computational resources.Due to the sensitivity and complexity of forecast models,long-term parameter forecasts(e.g.up to ...Forecasting environmental parameters in the distant future requires complex modelling and large computational resources.Due to the sensitivity and complexity of forecast models,long-term parameter forecasts(e.g.up to 2100)are uncommon and only produced by a few organisations,in heterogeneous formats and based on different assumptions of greenhouse gases emissions.However,data mining techniques can be used to coerce the data to a uniform time and spatial representation,which facilitates their use in many applications.In this paper,streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to(i)standardise and harmonise the data representations,(ii)produce intermediate scenarios and new informative parameters,and(iii)align all sets on a common time and spatial resolution.Time series crosscorrelation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas.Our results highlight that(i)the Mediterranean Sea may have a standalone‘response’to climate change with respect to other areas,(ii)the Poles are most representative of global forecasted change,and(iii)the trends are generally alarming for most oceans.展开更多
Global climate change may have serious impact on human activities in coastal and other areas.Climate change may affect the degree of storminess and,hence,change the wind-driven ocean wave climate.This may affect the r...Global climate change may have serious impact on human activities in coastal and other areas.Climate change may affect the degree of storminess and,hence,change the wind-driven ocean wave climate.This may affect the risks associated with maritime activities such as shipping and offshore oil and gas.So,there is a recognized need to understand better how climate change will affect such processes.Typically,such understanding comes from future projections of the wind and wave climate from numerical climate models and from the stochastic modelling of such projections.This work investigates the applicability of a recently proposed nonstationary fuzzy modelling to wind and wave climatic simulations.According to this,fuzzy inference models(FIS)are coupled with nonstationary time series modelling,providing us with less biased climatic estimates.Two long-term datasets for an area in the North Atlantic Ocean are used in the present study,namely NORA10(57 years)and ExWaCli(30 years in the present and 30 years in the future).Two distinct experiments have been performed to simulate future values of the time series in a climatic scale.The assessment of the simulations by means of the actual values kept for comparison purposes gives very good results.展开更多
基金financially supported by the Tehran University of Medical Sciences(project No:97-03-160-40156)
文摘Objective:To predict future trends in the incidence of malaria cases in the southeast of Iran as the most important area of malaria using Seasonal Autoregressive Integrated Moving Average(SARIMA)model,and to check the effect of meteorological variables on the disease incidence.Methods:SARIMA method was applied to fit a model on malaria incidence from April 2001 to March 2018 in Sistan and Baluchistan province in southeastern Iran.Climatic variables such as temperature,rainfall,rainy days,humidity,sunny hours and wind speed were also included in the multivariable model as covariates.Then,the best fitted model was adopted to predict the number of malaria cases for the next 12 months.Results:The best-fitted univariate model for the prediction of malaria in the southeast of Iran was SARIMA(1,0,0)(1,1,1)12[Akaike Information Criterion(AIC)=307.4,validation root mean square error(RMSE)=0.43].The occurrence of malaria in a given month was mostly related to the number of cases occurring in the previous 1(p=1)and 12(P=1)months.The inverse number of rainy days with 8-month lag(β=0.3292)and temperature with 3-month lag(β=-0.0026)were the best predictors that could improve the predictive performance of the univariate model.Finally,SARIMA(1,0,0)(1,1,1)12 including mean temperature with a 3-month lag(validation RMSE=0.414)was selected as the final multivariable model.Conclusions:The number of malaria cases in a given month can be predicted by the number of cases in the prior 1 and 12 months.The number of rainy days with an 8-month lag and temperature with a 3-month lag can improve the predictive power of the model.
文摘Objective:To determine the potential effect of environment variables on cutaneous leishmaniasis occurrence using time-series models and compare the predictive ability of seasonal autoregressive integrated moving average(SARIMA)models and Markov switching model(MSM).Methods:This descriptive study employed yearly and monthly data of 49364 parasitologically-confirmed cases of cutaneous leishmaniasis in Isfahan province,located in the center of Iran from January 2000 to December 2019.The data were provided by the leishmaniasis national surveillance system,the meteorological organization of Isfahan province,and Iranian Space Agency for vegetation information.The SARIMA and MSM models were implemented to examine the environmental factors of cutaneous leishmaniasis epidemics.Results:The minimum relative humidity,maximum relative humidity,minimum wind speed,and maximum wind speed were significantly associated with cutaneous leishmaniasis epidemics in different lags(P<0.05).Comparing SARIMA and MSM,Akaikes information criterion(AIC),and mean absolute percentage error(MAPE)in MSM were much smaller than SARIMA models(MSM:AIC=0.95,MAPE=3.5%;SARIMA:AIC=158.93,MAPE:11.45%).Conclusions:SARIMA and MSM can be a useful tool for predicting cutaneous leishmaniasis in Isfahan province.Since cutaneous leishmaniasis falls into one of two states of epidemic and non-epidemic,the use of MSM(dynamic)is recommended,which can provide more information compared to models that use a single distribution for all observations(Box-Jenkins SARIMA model).
文摘Water is a vital resource.It supports a multitude of industries,civilizations,and agriculture.However,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain is scarce.Therefore,it is crucial to forecast water demand to provide it to sectors either on regular or emergency days.The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions.This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future.Focusing on the collected data of Jeddah city,Saudi Arabia in the period between 2004 and 2018,we develop a hybrid approach that uses Artificial Neural Networks(ANN)for forecasting and Particle Swarm Optimization algorithm(PSO)for tuning ANNs’hyperparameters.Based on the Root Mean Square Error(RMSE)metric,results show that the(PSO-ANN)is an accurate model for multivariate time series forecasting.Also,the first day is the most difficult day for prediction(highest error rate),while the second day is the easiest to predict(lowest error rate).Finally,correlation analysis shows that the dew point is the most climatic factor affecting water demand.
基金supported by the National Key Research and Development Program of China(2023YFC3206300)the National Natural Science Foundation of China(42477529,42371145,42261026)+2 种基金the China-Pakistan Joint Program of the Chinese Academy of Sciences(046GJHZ2023069MI)the Gansu Provincial Science and Technology Program(22ZD6FA005)the National Cryosphere Desert Data Center(E01Z790201).
文摘Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.
基金This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the BlueBRIDGE project[grant agreement no 675680].
文摘Forecasting environmental parameters in the distant future requires complex modelling and large computational resources.Due to the sensitivity and complexity of forecast models,long-term parameter forecasts(e.g.up to 2100)are uncommon and only produced by a few organisations,in heterogeneous formats and based on different assumptions of greenhouse gases emissions.However,data mining techniques can be used to coerce the data to a uniform time and spatial representation,which facilitates their use in many applications.In this paper,streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to(i)standardise and harmonise the data representations,(ii)produce intermediate scenarios and new informative parameters,and(iii)align all sets on a common time and spatial resolution.Time series crosscorrelation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas.Our results highlight that(i)the Mediterranean Sea may have a standalone‘response’to climate change with respect to other areas,(ii)the Poles are most representative of global forecasted change,and(iii)the trends are generally alarming for most oceans.
文摘Global climate change may have serious impact on human activities in coastal and other areas.Climate change may affect the degree of storminess and,hence,change the wind-driven ocean wave climate.This may affect the risks associated with maritime activities such as shipping and offshore oil and gas.So,there is a recognized need to understand better how climate change will affect such processes.Typically,such understanding comes from future projections of the wind and wave climate from numerical climate models and from the stochastic modelling of such projections.This work investigates the applicability of a recently proposed nonstationary fuzzy modelling to wind and wave climatic simulations.According to this,fuzzy inference models(FIS)are coupled with nonstationary time series modelling,providing us with less biased climatic estimates.Two long-term datasets for an area in the North Atlantic Ocean are used in the present study,namely NORA10(57 years)and ExWaCli(30 years in the present and 30 years in the future).Two distinct experiments have been performed to simulate future values of the time series in a climatic scale.The assessment of the simulations by means of the actual values kept for comparison purposes gives very good results.
