ارزيابي دقت مدل‌هاي MLR، PCR، ARIMA و MLP در پيش‌بيني عمق نوري هواويزها

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

برآورد عمق نوري هواويزها (AOD) براي بررسي ميزان ذرات معلق موجود در جو كه يكي از آلاينده‌هاي هوا است استفاده مي‌شود. در اين پژوهش براي برآورد عمق نوري هواويزها در ايستگاه‌هاي فاقد تشعشع‌سنج و يا برآورد يك ساله (اتورگرسيو) در ايستگاه‌هاي داراي تشعشع‌سنج از مدل‌هاي مختلف همچون مدل‌هاي رگرسيون چند‌گانه (MLR)، رگرسيون مولفه‌هاي مبنا (PCR)، خودرگرسيون ميانگين متحرك انباشته (ARIMA) و نيز مدل شبكه عصبي مصنوعي (MLP)، استفاده شد. بدين منظور داده‌هاي دما، رطوبت نسبي، سرعت باد و ارتفاع لايه اتمسفري اخذ شده از پايگاه داده جهاني ECMWF در تراز 850 هكتوپاسكال به عنوان متغيرهاي مستقل و همچنين داده‌هاي تشعشع‌سنج خورشيدي اداره‌ هواشناسي شهرستان سنندج در بازه‌ي زماني 1/1/2005 تا 31/12/2016 به عنوان متغير وابسته در نظر گرفته شدند. نتايج نشان داد كه مدل ARIMA با دارا بودن مقادير عددي 0/91 R2=، 0/0501RMSE= و 0/033MAE= در مرحله آموزش مدل و نيز مقادير 0/89 R2=، 0/0586RMSE= و 0/0374MAE= در مرحله آزمون مدل داراي بهترين عملكرد در برآورد عمق نوري هواويزها در ايستگاه‌هاي فاقد تشعشع‌سنج است. همچنين نتايج مرحله اتورگرسيو نشان داد كه مدل MLP با دارا بودن مقادير عددي 0/96 R2=، 0/0483RMSE= و 0/028MAE= بالاترين دقت را از ميان مدل‌هاي فوق در برآورد عمق نوري هواويزها براي سال 2017داشته است.

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 nβ0, ..., 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=φβ_PCR+e Where φ is the matrix of base components, which is obtained as n * k, and βPCR 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