Forecasting rainfall against flood potential using linear regression in the case study of serang city

Gebyta Lee Yun Sindia, Yayat Ruhiat, Ganesha Antarnusa

Abstract


In 2022, Serang City experienced 166 floods, most of which occurred due to high rainfall. To anticipate and minimize the consequences that will occur by the flood disaster in 2023, rainfall forecasting is needed to forecast the potential flood areas in Serang City. The purpose of this research is to implement multiple linear regression analysis to forecast rainfall, forecast rainfall against flood potential, and identify the relationship between rainfall and flood events. The method of forecasting rainfall is done with multiple linear regression model analysis, Geographic Information System spatial analysis to forecast potential flood areas, and Pearson correlation test to identify the relationship between rainfall and flood events. The results show that rainfall in 2023 is predicted to be 66% of Serang City in the low category with rainfall values less than 1500 mm/year and 33% of other areas in the medium category with rainfall between 1500-2500 mm/year. In the same year, 9% of Serang City is predicted to have high flood potential, 25% medium, 1% low, and 64% is predicted to be safe from flooding. The relationship between rainfall and flood occurrence shows no correlation, so further research is needed.


Keywords


rainfall, Flood, forecasting, multiple linear regression, geographic information system

Full Text:

81-89

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