Water Level Prediction Using Different Numbers of Time Series Data Based on Chaos Approach

Mashuri, Adib; Adenan, Nur Hamiza; Karim, Nor Suriya Abd; Hamid, Nor Zila Abd

doi:10.13189/cea.2021.090221

Cited by 2 publications

(1 citation statement)

References 17 publications

(25 reference statements)

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“…Besides, flood discharge estimation can be done using the HEC RAS application, so the results of this study indicate an estimated increase in flood discharge according to the escalation in return time [5]. The research of Biantoro et al [6] used the FEDS flood detector to determine the amount of flood discharge and water level, while Mashuri et al [7] predicted water level using various time series data.…”

Section: Discussionmentioning

confidence: 98%

Analysis of Time of Concentration Estimates Using Some Methods and HEC RAS

Biantoro¹,

Wahyudi²,

Ni’am³

2023

cea

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Floods are natural disasters which cause losses that often occur in coastal cities such as Jakarta, Indonesia. Paying attention to rainfall and water flow is in the form of predicting floods. The research goal is to determine the best concentration time method, predict water level and flood inundation, and reduce the impact of flooding for people in the downstream area of the river, Jakarta. The method used in this research is quantitative descriptive analysis, rational approach, time of concentration, artificial neural network (ANN), and the use of the HEC RAS application. The results show that the arrival time of the flood using the Kirpich method shows a concentration time of 12 hours 5 minutes, Travel Time of 12 hours 10 minutes, Velocity of 11 hours 21 minutes and Nakayasu of 9 hours 1 minute. Modelling using an artificial neural network shows that the modelling results can function to predict water levels in the future. The results of the artificial neural network that estimates the water level for the 2020 period are 5.1 m (January), 4.94 m (February), 3.89 m (March), 3.52 m (April), 3.71 m (May), 2.58 m (June), 2.59 m (July), 24.1 m (August), 2.97 m (September), 3.32 m (October), 3.07 m (November) and 3.68 m (December).

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Section: Discussionmentioning

confidence: 98%