The flood prediction model using Artificial Neural Network (ANN) and weather Application Programming Interface (API) as an alternative effort to flood mitigation in the Jenelata Sub-watershed

Abstract

Jenelata is a sub of the Jeneberang watershed in South Sulawesi which has rainfall intensity from 2,800 mm to 4,000 mm per year on the upstream area, some of the rainfall occurs in a short period with high intensity, resulting significant rise to river water level. It potentially to cause floods downstream. Topography of the upstream area is mountainous with an average slope of ± 0.024 along the river, with flow length of 38,314 km it has a large velocity flow. Purpose of this research is to providing alternative for flood mitigation using rainfall predictions and runoff calculations, hypoteticaly it can reduce the impact of possible flood. This research was conducted using Artificial Neural Network (ANN) as rainfall predictor and input variable for runoff calculation using SCS method. ANN input variable will use weather prediction data from the global weather API, while SCS method will calculate maximum runoff in the catchment for the next 24 hours. Results of the rainfall prediction model get deviation of 28.81 mm and accuracy of 58% compared to the observations. Meanwhile, runoff model discharge acquires deviation of 1181.7 m3 and water level ± 0.19 m at the designated location of water level gauge.