An improved flood forecasting system with cluster based visualization and analyzing using GK-ANFIS and CGDNN
作者：
Highlights：
• This work has proffered an efficient FF technique centred on flood DV and also DA.
• The work has developed the GK-ANFIS visualization technique and CGDNN based FF.
• Developed approach can manage circumstances and predict the flood accurately.
• The CGDNN technique 96.66% accuracy, 96.38% precision, and 96.35% f-measure.
• This work can establish more robust visualization and also forecasting methods in future.
摘要
•This work has proffered an efficient FF technique centred on flood DV and also DA.•The work has developed the GK-ANFIS visualization technique and CGDNN based FF.•Developed approach can manage circumstances and predict the flood accurately.•The CGDNN technique 96.66% accuracy, 96.38% precision, and 96.35% f-measure.•This work can establish more robust visualization and also forecasting methods in future.
论文关键词：Forecasting,Feature extraction,levy flight k means clustering (LF-K-Means),Gaussian Kernel Adaptive Neuro Fuzzy Interface System (GK-ANFIS),Conjugate gradient deep neural network (CGDNN),K-Fold Cross Validation
论文评审过程：Received 5 February 2022, Revised 24 August 2022, Accepted 31 August 2022, Available online 6 September 2022, Version of Record 18 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118747
