Predicting the shear strength of concrete beam through ANFIS-GA–PSO hybrid modeling

Li, Jie; Yan, Gongxing; Abbud, Luay Hashem; Alkhalifah, Tamim; Alturise, Fahad; Khadimallah, Mohamed Amine; Marzouki, Riadh

doi:10.1016/j.advengsoft.2023.103475

Cited by 19 publications

(1 citation statement)

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“…This research shows that ANIFS is very good for predicting volatile data and spikes, such as rainfall data. Several studies have produced the same conclusions about the ANFIS method (Li et al, 2023;Saleh et al, 2023;Samantaray et al, 2023;Sangar et al, 2024).…”

Section: Rainfall Modeling Using the Anfis Methodsmentioning

confidence: 72%

Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters

Fauzi,

Erlinda,

Arum

2024

JTAM

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Hydrometeorological disasters are one of the disasters that often occur in big cities like Semarang. Hydrometeorological disasters that often occur are floods caused by high-intensity rainfall in the area. Early mitigation needs to be done by knowing about future rain. Rainfall data in Semarang City fluctuates, so the Adaptive Neuro-Fuzzy Inference System (ANFIS) method approach is very appropriate. This research will use the Grid Partitioning (GP) approach to produce more accurate forecasting. The data used in this research is daily rainfall observation data from the Meteorology Climatology Geophysics Agency (BMKG). The membership functions used are Gaussian and Generalized Bell. The two membership functions will be compared based on the RMSE and MAPE values to get the best one. The data used in this research is daily rainfall data. Rainfall in Semarang City every month experiences anomalies, which can result in flood disasters. The ANFIS-GP method with a Gaussian membership function is the best, with an RMSE value of 0.0898 and a MAPE of 5.2911. Based on the forecast results for the next thirty days, a rainfall anomaly of 102.53 mm on the thirtieth day could cause a flood disaster.

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Section: Rainfall Modeling Using the Anfis Methodsmentioning

confidence: 72%