The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. The...The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.展开更多
Water resource allocation （WRA） is a useful but complicated topic in water resource management. With the targets set out in the Plan of Newly Increasing Yield （NIY） of 10×1011 Jin （1 kg=2 Jin） from 2009 to ...Water resource allocation （WRA） is a useful but complicated topic in water resource management. With the targets set out in the Plan of Newly Increasing Yield （NIY） of 10×1011 Jin （1 kg=2 Jin） from 2009 to 2020, the immediate question for the Songhua River Region （SHRR） is whether water is sufficient to support the required yield increase. Very few studies have considered to what degree this plan influences the solution of WRA and how to adapt. This paper used a multi-objective programming model for WRA across the Harbin region located in the SHRR in 2020 and 2030 （p=75%）. The Harbin region can be classified into four types of sub-regions according to WRA： Type I is Harbin city zone. With rapid urbanization, Harbin city zone has the highest risk of agricultural water shortage. Considering the severe situation, there is little space for Harbin city zone to reach the NIY goal. Type II is sub-regions including Wuchang, Shangzhi and Binxian. There are some agricultural water shortage risks in this type region. Because the water shortage is relatively small, it is possible to increase agricultural production through strengthening agricultural water-saving countermeasures and constructing water conservation facilities. Type III is sub-regions including Acheng, Hulan, Mulan and Fangzheng. In this type region, there may be a water shortage if the rate of urbanization accelerates. According to local conditions, it is needed to enhance water-saving countermeasures to increase agricultural production to a certain degree. Type IV is sub-regions including Shuangcheng, Bayan, Yilan, Yanshou and Tonghe. There are good water conditions for the extensive development of agriculture. Nevertheless, in order to ensure an increase in agricultural production, it is necessary to enhance the way in which water is utilized and consider soil resources. These results will help decision makers make a scientific NIY plan for the Harbin region for sustainable utilization of regional water resources and an increase in agricultural production.展开更多
文摘The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.
基金the Knowledge Innovation Project of Chinese Academy of Sciences (NO.KZCX2-YW-Q06-1-3)the Ministry of Science and Technology of China for"973"project(NO.2010CB428404)
文摘Water resource allocation （WRA） is a useful but complicated topic in water resource management. With the targets set out in the Plan of Newly Increasing Yield （NIY） of 10×1011 Jin （1 kg=2 Jin） from 2009 to 2020, the immediate question for the Songhua River Region （SHRR） is whether water is sufficient to support the required yield increase. Very few studies have considered to what degree this plan influences the solution of WRA and how to adapt. This paper used a multi-objective programming model for WRA across the Harbin region located in the SHRR in 2020 and 2030 （p=75%）. The Harbin region can be classified into four types of sub-regions according to WRA： Type I is Harbin city zone. With rapid urbanization, Harbin city zone has the highest risk of agricultural water shortage. Considering the severe situation, there is little space for Harbin city zone to reach the NIY goal. Type II is sub-regions including Wuchang, Shangzhi and Binxian. There are some agricultural water shortage risks in this type region. Because the water shortage is relatively small, it is possible to increase agricultural production through strengthening agricultural water-saving countermeasures and constructing water conservation facilities. Type III is sub-regions including Acheng, Hulan, Mulan and Fangzheng. In this type region, there may be a water shortage if the rate of urbanization accelerates. According to local conditions, it is needed to enhance water-saving countermeasures to increase agricultural production to a certain degree. Type IV is sub-regions including Shuangcheng, Bayan, Yilan, Yanshou and Tonghe. There are good water conditions for the extensive development of agriculture. Nevertheless, in order to ensure an increase in agricultural production, it is necessary to enhance the way in which water is utilized and consider soil resources. These results will help decision makers make a scientific NIY plan for the Harbin region for sustainable utilization of regional water resources and an increase in agricultural production.
