The rapid pace of development of GIS （geographical information system） has assisted in identification of conservation priority sites by delineating species distribution using models on habitat suitability. Gaur, Bos...The rapid pace of development of GIS （geographical information system） has assisted in identification of conservation priority sites by delineating species distribution using models on habitat suitability. Gaur, Bos gaurus, is categorized as ＂Vulnerable＂ in the IUCN Red List of Threatened Species, 2009. The study has used ENFA （ecological niche factor analysis） to understand the distribution status of Gaur in TATR （Tadoba-Andhari Tiger Reserve）, Central India. TATR was sampled using stratified random sampling strategy. A total of 21 continuous variables were used, categorised under 4 environmental descriptors categories viz. habitat, anthropogenic, topographic and hydrological variables. All the variables were tested for the correlation and one of the variable among strongly correlated （r 〉 0.7） variables was discarded to avoid redundancy. A total of 14 variables were retained. The model resulted in marginality of 0.56 and specialization of 2.608. Presence of Gaur showed the positive association with canopy density classes （〈 30% ＆ 40-60%） and open forest. However, it was negatively associated with elevation, non-forest, riparian forest, scrub and teak forest. The study has delineated the areas where appropriate habitat conditions exist to sustain Gaur populations vital for planning strategies for conservation of this megaherbivore species in tropical forests.展开更多
The difficulty in crude oil price forecasting, due to inherent complexity, has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forec...The difficulty in crude oil price forecasting, due to inherent complexity, has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting crude oil prices. However, all of the existing models of prediction can not meet practical needs. Very recently, Wang and Yu proposed a new methodology for handling complex systems-TEI@I methodology by means of a systematic integration of text mining, econometrics and intelligent techniques.Within the framework of TEI@I methodology, econometrical models are used to model the linear components of crude oil price time series (i.e., main trends) while nonlinear components of crude oil price time series (i.e., error terms) are modelled by using artificial neural network (ANN) models. In addition, the impact of irregular and infrequent future events on crude oil price is explored using web-based text mining (WTM) and rule-based expert systems (RES) techniques. Thus, a fully novel nonlinear integrated forecasting approach with error correction and judgmental adjustment is formulated to improve prediction performance within the framework of the TEI@I methodology. The proposed methodology and the novel forecasting approach are illustrated via an example.展开更多
文摘The rapid pace of development of GIS （geographical information system） has assisted in identification of conservation priority sites by delineating species distribution using models on habitat suitability. Gaur, Bos gaurus, is categorized as ＂Vulnerable＂ in the IUCN Red List of Threatened Species, 2009. The study has used ENFA （ecological niche factor analysis） to understand the distribution status of Gaur in TATR （Tadoba-Andhari Tiger Reserve）, Central India. TATR was sampled using stratified random sampling strategy. A total of 21 continuous variables were used, categorised under 4 environmental descriptors categories viz. habitat, anthropogenic, topographic and hydrological variables. All the variables were tested for the correlation and one of the variable among strongly correlated （r 〉 0.7） variables was discarded to avoid redundancy. A total of 14 variables were retained. The model resulted in marginality of 0.56 and specialization of 2.608. Presence of Gaur showed the positive association with canopy density classes （〈 30% ＆ 40-60%） and open forest. However, it was negatively associated with elevation, non-forest, riparian forest, scrub and teak forest. The study has delineated the areas where appropriate habitat conditions exist to sustain Gaur populations vital for planning strategies for conservation of this megaherbivore species in tropical forests.
基金This research is partially supported by NSFC, CAS, RGC of Hong Kong and Ministry of Education and Technology of Japan
文摘The difficulty in crude oil price forecasting, due to inherent complexity, has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting crude oil prices. However, all of the existing models of prediction can not meet practical needs. Very recently, Wang and Yu proposed a new methodology for handling complex systems-TEI@I methodology by means of a systematic integration of text mining, econometrics and intelligent techniques.Within the framework of TEI@I methodology, econometrical models are used to model the linear components of crude oil price time series (i.e., main trends) while nonlinear components of crude oil price time series (i.e., error terms) are modelled by using artificial neural network (ANN) models. In addition, the impact of irregular and infrequent future events on crude oil price is explored using web-based text mining (WTM) and rule-based expert systems (RES) techniques. Thus, a fully novel nonlinear integrated forecasting approach with error correction and judgmental adjustment is formulated to improve prediction performance within the framework of the TEI@I methodology. The proposed methodology and the novel forecasting approach are illustrated via an example.
