基于进化遗传优化的自适应神经模糊推理新系统与地理信息系统的非洲山地土壤有机碳储量估算和绘图

Soil organic carbon (SOC) pool has the potential to mitigate or enhance climate change by either acting as a sink,or a source of atmospheric carbon dioxide (CO2) and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth.As such,the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system (ANFIS-EG) in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve,Kenya.Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model,which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest Reserve.This was achieved with a reasonable level of uncertainty (i.e.,root mean square error of 15.07 Mg C ha-1),hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies.There is potential for improving the model performance,as indicated by the current ratio of performance to deviation (1.6).The mnapping also revealed marginally higher SOC stocks in the forested ecosystems (i.e.,an average of 109.78 Mg C ha-1) than in the agro-ecosystems (i.e.,an average of 95.9 Mg C ha-1).

A novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system and geographical information systems predict and map soil organic carbon stocks across an Afromontane landscape

Soil organic carbon (SOC) pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide (CO₂) and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth. As such, the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system (ANFIS-EG) in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve, Kenya. Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model, which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest Reserve. This was achieved with a reasonable level of uncertainty (i.e., root mean square error of 15.07 Mg C ha^-1), hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies. There is potential for improving the model performance, as indicated by the current ratio of performance to deviation (1.6). The mapping also revealed marginally higher SOC stocks in the forested ecosystems (i.e., an average of 109.78 Mg C ha^-1) than in the agro-ecosystems (i.e., an average of 95.9 Mg C ha^-1).