Application of artificial neural networks in global climate change and ecological research:An overview

Fields that employ artificial neural networks(ANNs)have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence.ANN has been adopted widely and put into practice by research-ers in light of increasing concerns over ecological issues such as global warming,frequent El Nio-Southern Oscillation(ENSO)events,and atmospheric circulation anomalies.Limitations exist and there is a potential risk for misuse in that ANN model pa-rameters require typically higher overall sensitivity,and the chosen network structure is generally more dependent upon individ-ual experience.ANNs,however,are relatively accurate when used for short-term predictions;despite global climate change re-search favoring the effects of interactions as the basis of study and the preference for long-term experimental research.ANNs remain a better choice than many traditional methods when dealing with nonlinear problems,and possesses great potential for the study of global climate change and ecological issues.ANNs can resolve problems that other methods cannot.This is especially true for situations in which measurements are difficult to conduct or when only incomplete data are available.It is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research.

Fields that employ artificial neural networks （ANNs） have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence. ANN has been adopted widely and put into practice by research- ers in light of increasing concerns over ecological issues such as global warming, frequent E1 Nifio-Southern Oscillation （ENSO） events, and atmospheric circulation anomalies. Limitations exist and there is a potential risk for misuse in that ANN model parameters require typically higher overall sensitivity, and the chosen network structure is generally more dependent upon individual experience. ANNs, however, are relatively accurate when used for short-term predictions; despite global climate change research favoring the effects of interactions as the basis of study and the preference for long-term experimental research. ANNs remain a better choice than many traditional methods when dealing with nonlinear problems, and possesses great potential for the study of global climate change and ecological issues. ANNs can resolve problems that other methods cannot. This is especially true for situations in which measurements are difficult to conduct or when only incomplete data are available. It is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research.