Climatology
Ali Mohammad khorshiddoust; Kaveh Mohammadpour; Seyed Asaad Hosseini
Abstract
Introduction Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact ...
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Introduction Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact on mortality and mortality, and the use of predictive models can be used to identify fluctuations in climatic parameters affecting disease and their prevalence and planning and Compatibility with the environment to be effective. Methodology Using of predictive models can be consider as an effective tool in managing and controlling the diseases, reducing mortality and planning. Recent study used from Artificial Neural Networks and Logistic Regression models as an effective tool in the prediction of nonlinear processes to predict the rate of asthma admissions related to Climatic parameters in Sanandaj/Sine city. Used data during period of 8-years (2001-2008) collected from synoptic station and Toheid and Beasat hospitals in the Sanandaj/Sine city. Then, the climatic parameters and rate of asthma admissions considered as an input and output data of models, respectively. Result and Discussion The results of the output of two nonlinear models of artificial neural network and Logit in examining the effect of climatic parameters on the number of the asthma patients in Sanandaj/Sine showed that the monthly average parameters with high coefficient of determination (R2=0.98) of temperature (average, minimum, maximum) and QFE pressure in the artificial neural network model and The monthly average minimum temperature, QFF pressure and wind speed (in Knot) in the Logit model have had the greatest impact on the rate of asthma admissions in the city. As the wind speed in the Logit model is more effective than other climatic parameters, that it is clear with the logarithmic superiority (-0.977) and the Wald coefficient (85.616). In general, air pressure, temperature and wind speed are the most effective climatic parameters on the number of asthma patients visiting the hospital. Therefore, depending on the accuracy of the models, the above argument means that among the parameters examined, the elements are more important than other parameters in the city. As the climatic elements have a more effective role in the admission patients to the hospital, and their fluctuations will be more significant in patients' fluctuations. The effects of environmental parameters (climate and pollutants) on diseases have previously been investigated as well, so that the results of previous logistic regression have display a increase respiratory disease, vulnerability of children to asthma and an increase in allergies; In the present study, the results of Logit model (69.5%) also indicate that decrease in the average minimum temperature lead to a decrease in the number of the asthma patients, it means that the rate of asthma is more less in temperatures close to zero or higher and vice versa, the admission more higher in the colder temperature (below zero); in the other words, the more balanced the temperature has the lower the rate, and in the colder the ambient temperature has the highest the number of asthma patients. Thus, comparison the present results and previous studies show that admissions change depending on climate, geographic position and the fluctuation of the elements and then the specific geographical location and the different climatic types of a region will play a decisive role in the number of asthma visitors to hospital. Conclusion The results indicated that Artificial Neural Network model predicted the asthma admissions related to monthly minimum, maximum and average temperatures with considerable accuracy, so that the correlation between actual and predicted data is significant with 0.01 coefficient and 0.99 confidence. Also, Input parameters in the Logit method shows that the rate of asthma admissions affected by parameters of average minimum temperature, average pressure QFF and average wind speed (in knot). In other words, the logarithmic ratio of each of cited parameters is significant with β-coefficients (-0.517), (-0.734) and (-0.977), respectively, that throughout of studied parameters is wind element of effective in asthma admissions then others to the hospital. In general, Artificial Neural Network model showed more sufficiency and accuracy than Logit model. As a result, both Logistic Regression and the Artificial Neural Network methods show that climatic parameters have a greater than 50% effect on the number of asthma patients referred to the hospital (the accuracy models: 69.5 and 98, respectively). In the Artificial Neural Network model, the most accurate possible result shows the more effective role of climatic parameters of temperature and air pressure on the asthma patients. Also, filtering the parameters examined at the output of the Logistic model showed the most possible coefficients for minimum temperature, QFF air pressure and wind speed (knot), among which wind speed was the most important element. Finally, the accuracy of the models showed that the Artificial Neural Network model has a higher accuracy depending on the coefficient of determination and highest correlation. Thus, Artificial Neural Network and Logit as nonlinear methods could well predict the relationship between climatic parameters and the number of the asthma patients. Also, according to the appropriate selection of input parameters and determination of different structures in the neural network is possible to design different models with the highest efficiency and can be considered as an effective and powerful tool in estimating similar studies. Introduction Prediction of hospital admissions related to climatic parameters is discussed matters that in recent decades in result from climate change, urbanization and air pollution has triggered widespread in many societies. Fluctuations in climatic parameters, in turn, can have a significant impact on mortality and mortality, and the use of predictive models can be used to identify fluctuations in climatic parameters affecting disease and their prevalence and planning and Compatibility with the environment to be effective. Methodology Using of predictive models can be consider as an effective tool in managing and controlling the diseases, reducing mortality and planning. Recent study used from Artificial Neural Networks and Logistic Regression modelsasan effective toolinthe prediction ofnonlinearprocessesto predict the rate of asthma admissions related to Climatic parameters in Sanandaj/Sine city. Used data during period of 8-years (2001-2008) collected from synoptic station and Toheid and Beasat hospitals in the Sanandaj/Sine city. Then, the climatic parameters and rate of asthma admissions considered as an input and output data of models, respectively. Result and Discussion The results of the output of two nonlinear models of artificial neural network and Logit in examining the effect of climatic parameters on the number of the asthma patients in Sanandaj/Sine showed that the monthly average parameters with high coefficient of determination (R2=0.98) of temperature (average, minimum, maximum) and QFE pressure in the artificial neural network model and The monthly average minimum temperature, QFF pressure and wind speed (in Knot) in the Logit model have had the greatest impact on the rate of asthma admissions in the city. As the wind speed in the Logit model is more effective than other climatic parameters, that it is clear with the logarithmic superiority (-0.977) and the Wald coefficient (85.616). In general, air pressure, temperature and wind speed are the most effective climatic parameters on the number of asthma patients visiting the hospital. Therefore, depending on the accuracy of the models, the above argument means that among the parameters examined, the elements are more important than other parameters in the city. As the climatic elements have a more effective role in the admission patients to the hospital, and their fluctuations will be more significant in patients' fluctuations. The effects of environmental parameters (climate and pollutants) on diseases have previously been investigated as well, so that the results of previous logistic regression have display a increase respiratory disease, vulnerability of children to asthma and an increase in allergies; In the present study, the results of Logit model (69.5%) also indicate that decrease in the average minimum temperature lead to a decrease in the number of the asthma patients, it means that the rate of asthma is more less in temperatures close to zero or higher and vice versa, the admission more higher in the colder temperature (below zero); in the other words, the more balanced the temperature has the lower the rate, and in the colder the ambient temperature has the highest the number of asthma patients. Thus, comparison the present results and previous studies show that admissions change depending on climate, geographic position and the fluctuationof the elements and then the specific geographical location and the different climatic types of a region will play a decisive role in the number of asthma visitors to hospital. Conclusion The results indicated that Artificial Neural Network model predicted the asthma admissions related to monthly minimum, maximum and average temperatures with considerable accuracy, so that the correlation between actual and predicted data is significant with 0.01coefficient and0.99 confidence.Also, Input parameters in the Logit method shows that the rate of asthma admissions affected by parameters of average minimum temperature, average pressure QFF and average wind speed (in knot). In other words, the logarithmicratio ofeach of citedparametersissignificant with β-coefficients (-0.517), (-0.734)and(-0.977), respectively, thatthroughoutofstudied parametersis windelement of effective in asthma admissionsthen others to thehospital. In general, ArtificialNeural Networkmodelshowed more sufficiencyandaccuracy than Logitmodel. As a result, both Logistic Regression and the Artificial Neural Network methods show that climatic parameters have a greater than 50% effect on the number of asthma patients referred to the hospital (the accuracy models: 69.5 and 98, respectively). In the Artificial Neural Network model, the most accurate possible result shows the more effective role of climatic parameters of temperature and air pressure on the asthma patients. Also, filtering the parameters examined at the output of the Logistic model showed the most possible coefficients for minimum temperature, QFF air pressure and wind speed (knot), among which wind speed was the most important element. Finally, the accuracy of the models showed that the Artificial Neural Network model has a higher accuracy depending on the coefficient of determination and highest correlation. Thus, Artificial Neural Network and Logit as nonlinear methods could well predict the relationship between climatic parameters and the number of the asthma patients. Also, according to the appropriate selection of input parameters and determination of different structures in the neural network is possible to design different models with the highest efficiency and can be considered as an effective and powerful tool in estimating similar studies.
Climatology
Ali Mohammad Khorshiddoust; Fariba Esfandeyari; Seyed Asaad Hosseini; Parvaneh Dolatkhah
Volume 22, Issue 65 , November 2018, , Pages 141-162
Abstract
One of the new techniques in the field of predicting hydrological and geomorphologic processes is artificial neural network from the components of artificial intelligence which are trying to implement the amazing features of human brain in an artificial system and are powerful tools in the field of modeling ...
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One of the new techniques in the field of predicting hydrological and geomorphologic processes is artificial neural network from the components of artificial intelligence which are trying to implement the amazing features of human brain in an artificial system and are powerful tools in the field of modeling and predicting geomorphologic parameters and in this study have been used for the prediction of sediment in Aras basin. For this purpose was used information of discharge, sedimentation and prediction monthly on Borran hydrometric station located in the Basin of Darreh Roud that is from the main sub basin of Aras river in Moghan plain during the period of 34 years (water year of 53-54 to 86-87). So that the discharge and precipitation rate as inputs to the neural network and sediment was considered the output of network. For this purpose used the facilities and functions available in programming environment MATLAB / 2010 and SPSS / 21 software. Then models were evaluated through statistical parameters such as the determination coefficient, root mean square error, mean square error, mean absolute error, correlation coefficient and also mean percentage relative error. The results, in addition to confirming the capability of artificial neural network model, showed that, there is good correspondence between predicted values and observed data. So that the error mean of this model with the observed data is 0.9 and correlation coefficient is 0.99 which is significant at 0.01.The results of this study showed that the artificial neural. Network model has more accuracy in the estimation of sediment at the investigated basin. The results can be useful in planning and management of water and watersheds and natural resource management, especially in agriculture, industry, drinking and Forecast of Reservoir Sedimentation
Climatology
Mahmoud Houshyar; Behrooz Sobhani; Seyed Asaad Hosseini
Abstract
With the seriousness of the climate change debate in the world, the study of parameters and elements of the climate has been widely considered. With changes in climate patterns and changes in temperature and precipitation patterns, other components such as runoff and soil moisture, which are important ...
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With the seriousness of the climate change debate in the world, the study of parameters and elements of the climate has been widely considered. With changes in climate patterns and changes in temperature and precipitation patterns, other components such as runoff and soil moisture, which are important for natural and human systems, will undergo metamorphosis. Therefore, long-term prediction of climatic variables has been considered by many scientific communities worldwide in order to know about their changes and considering the necessary measures to moderate the adverse effects of climate change. The phenomenon of climate change is of increasing importance due to its scientific and practical dimensions, since human systems dependent on climatic elements such as agriculture, industry and the like are designed and operated on the basis of the stability and stability of the climate. Accordingly, general circulation models (GCMs) have been developed. Although these models represent significant results on the atmospheric and continental spatial scales, they combine a large part of the complexity of the planet's system, but they are inherently unable to control the dynamics and forms with a fine grid Local scalability. Therefore, an assessment of the effect of climate change on a local scale requires an interim and spatial gap between large-scale climatic variables and meteorological variables with local scale, in which case the main approach is the same downscaling models. The SDSM model is one of the most widely used statistical microscopic instruments, which has many uses in meteorological, hydrological, geographic and environmental studies. Because in this method, large-scale daily circulation patterns are used on a stationary scale; and when used for the rapid and cost-effective estimation of climate change, and for randomized meteorological generators and modified functions, have given acceptable results. Given that global models have generally simulated climatic elements until the year 2100, it is possible to use global model data to simulate the desired variables such as precipitation and temperature on a station scale. The Intergovernmental Panel on Climate Change (IPCC) has used its latest assessment report (AR5) on new scenarios for the RCP as representatives of different levels of greenhouse gas emissions. The new emission scenarios have four key paths RCP2.6, RCP4.5, RCP6 and RCP8.5, which are named after their radiation in 2100, Future Perspective. The variation of the maximum temperatures of the synoptic station of Urmia during the period (2021-2050) of the CanESM2 global model has been used under three scenarios RCP2.6, RCP4.5 and RCP8.5.
