Establishment of Dynamic Evolving Neural-Fuzzy Inference System Model for Natural Air Temperature Prediction
نویسندگان
چکیده
Air temperature (AT) prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, weather forecasting. This study applied compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy system (DENFIS), hybrid (HyFIS), adaptive neurofuzzy (ANFIS) for AT prediction. Modelling was done stations North Dakota (ND), USA, Robinson, Ada, Hillsboro. The results reveal that FIS type models are well suited when handling highly variable data, such as AT, which shows high positive correlation with average daily dew point (DP), total solar (TSR), negative wind speed (WS). At Robinson station, DENFIS performed best coefficient determination (R2) 0.96 modified index agreement (md) 0.92, followed by ANFIS R2 0.94 md 0.89, HyFIS 0.90 0.84. A similar result observed other two stations, Ada Hillsboro where R2: 0.953/0.960, md: 0.903/0.912, then 0.943/0.942, 0.888/0.890, 0.908/0.905, 0.845/0.821, respectively. It be concluded all capable predicting efficiency only using DP, TSR, WS input variables. makes application these more reliable meteorological need least number valuable areas climatological seasonal variations studied will allow providing excellent error margin without huge expenditure.
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ژورنال
عنوان ژورنال: Complexity
سال: 2022
ISSN: ['1099-0526', '1076-2787']
DOI: https://doi.org/10.1155/2022/1047309