Assessing the accuracy of MLR, PCR, ARIMA, and MLP in predicting the aerosols optical depth

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Sajedeh Sajedeh; AMANOLLAHI JAMIL; DARAND MOHAMMAD

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Journal: Journal of Environmental Studies Year:2018 | Volume:44 | Issue:3 Page(s): 461-472

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Title

Assessing the accuracy of MLR, PCR, ARIMA, and MLP in predicting the aerosols optical depth

Author(s)

Sajedeh Sajedeh | AMANOLLAHI JAMIL | DARAND MOHAMMAD | Issue Writer Certificate

Keywords

ECMWF databaseQ1

forecastingQ1

Aerosol Optical DepthQ1

Autoregressive integrated moving averageQ1

Artificial Neural NetworksQ1

Abstract

Introduction: Atmospheric aerosols have different sources that we can refer to volcanic activities, dust, salt particles in the seas and oceans, or they due to human activities that we can refer to activities that such as industrial activities, transportation, fuel costs and … . aerosols have very important role in transitive radiation and chemical process that they are the earth’ s climate controller. Among the internationally-conducted works in this area can refer to the Olcese et al., 2015 which have been done based on the use of the artificial neural network model (MLP). They used previous values of the AOD at two stations as input of artificial neural network model to estimate the AOD under cloudy conditions and in situations where little data is available. This method was used to predict the values of AOD on nine stations with 440nm wavelengths on the east coast of the United States during the 1999 to 2012. The calculated R2=0. 85 between the observed and predicted AOD indicate a good performance of this model. To date, there is no research to estimate AOD by using different models such as Multiple linear Regression, Principal Component Regression, Artificial Neural Networks and Autoregressive integrated moving average model in Iran. Therefor in this research estimation AOD examined in two cases including estimate for areas with no Pyranometer stations and long-lasting estimation in stations with solar radiation detector for the future under. Material and methods: In this study related data to Pyranometer were collected for understudied are though the Meteorology office in center of Kurdistan province ranged 2005/01/01 until 2016/12/31. Thus, the total number of available data for the mentioned time period was 4382 data in the study area, and since there was no solar radiation for some days of the year, the total number of data used for Sanandaj city was reduced to 3956. Study area: Sanandaj is the capital of Kurdistan province. About geographic location this city is located in within limits 35 degree and 20 minutes north latitude and 47 degree east longitude from Greenwich Hour circle and in the 1373/4 meters height above sea level. Multiple linear Regression Model: In the Multiple linear Regression turn to check the relation between a dependent variable and several independent by earned relationship for them in the SPSS software, in the Multiple linear Regression the measure of AOD serve as dependent variable and meteorology numeral quantity such as temperature, relative humidity, wind speed and also altitude atmosphere were considered as independent variable. The general formula for the MLR model is as follows: Y=β _0+β _1 x_1+⋯ +β _n x_n+ε In this case, y is dependent variable. X1, . . ., Xn denote the independent variables, and also nβ 0, . . ., β report the fixed constants. Ԑ also indicates the remaining values. Principal Component Regression Model: Principal Component Regression Model is a combination of Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). These calculations are as follows: Y=φ β _PCR+e Where φ is the matrix of base components, which is obtained as n * k, and β PCR represents the first of the components of the K score. The vector of e is a random error which defined as n٭ 1. Mark and scores for the components are based on the original version of the OLS method as follows: β _PCR=(φ ^' 〖 φ )〗 ^(-1) φ ^' y=(L^2 )^(-1) φ ^' y In this case, L2 is the amount of slice of the matrix, which is based on the Kth parameter, which also indicates the slip of the parameter k⅄ . Finally, the following equation was reached. β _PCR=∑ _(K=1)^K▒ (υ _k u_k^')/d_k y, K

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APA: Copy

Sajedeh, Sajedeh, AMANOLLAHI, JAMIL, & DARAND, MOHAMMAD. (2018). Assessing the accuracy of MLR, PCR, ARIMA, and MLP in predicting the aerosols optical depth. JOURNAL OF ENVIRONMENTAL STUDIES, 44(3 ), 461-472. SID. https://sid.ir/paper/3384/en

Vancouver: Copy

Sajedeh Sajedeh, AMANOLLAHI JAMIL, DARAND MOHAMMAD. Assessing the accuracy of MLR, PCR, ARIMA, and MLP in predicting the aerosols optical depth. JOURNAL OF ENVIRONMENTAL STUDIES[Internet]. 2018;44(3 ):461-472. Available from: https://sid.ir/paper/3384/en

IEEE: Copy

Sajedeh Sajedeh, JAMIL AMANOLLAHI, and MOHAMMAD DARAND, “Assessing the accuracy of MLR, PCR, ARIMA, and MLP in predicting the aerosols optical depth,” JOURNAL OF ENVIRONMENTAL STUDIES, vol. 44, no. 3 , pp. 461–472, 2018, [Online]. Available: https://sid.ir/paper/3384/en

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