This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in th...This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.展开更多
根据山西省境内69个CRU（Climate Research Unit）网格点1951—2012时段的降水数据（精度为0.5°×0.5°）,采用主成分（PC）、旋转主成分（RPC）分析对山西省6—9月降水进行时空特征分析。结果表明,山西省6—9月降水整体...根据山西省境内69个CRU（Climate Research Unit）网格点1951—2012时段的降水数据（精度为0.5°×0.5°）,采用主成分（PC）、旋转主成分（RPC）分析对山西省6—9月降水进行时空特征分析。结果表明,山西省6—9月降水整体呈减少趋势,但是南北地区变化存在差异。单月降水主要表现出3-4个空间型,即山西省南部、北部、中部（中西部、中东部）。与亚洲经向（MC）和纬向（ZC）环流指数相关分析结果显示,研究区主要降水来源于偏南气流带来的水汽。西风的增强能影响6、7月山西省北部地区,9月山西省南部地区的降水。9月中南部地区降水受到来自中国东海的东北方向气流影响。展开更多
文摘This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.
文摘根据山西省境内69个CRU（Climate Research Unit）网格点1951—2012时段的降水数据（精度为0.5°×0.5°）,采用主成分（PC）、旋转主成分（RPC）分析对山西省6—9月降水进行时空特征分析。结果表明,山西省6—9月降水整体呈减少趋势,但是南北地区变化存在差异。单月降水主要表现出3-4个空间型,即山西省南部、北部、中部（中西部、中东部）。与亚洲经向（MC）和纬向（ZC）环流指数相关分析结果显示,研究区主要降水来源于偏南气流带来的水汽。西风的增强能影响6、7月山西省北部地区,9月山西省南部地区的降水。9月中南部地区降水受到来自中国东海的东北方向气流影响。
