The Application of Artificial Neural Network for Flood Systems Mitigation at Jakarta City

Sardjono, Wahyu; Perdana, Widhilaga Gia

doi:10.1109/icimtech.2019.8843735

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In [1], the author used data on flooding in the city of Jakarta and developed a model that will be used to predict the rainfall and prevent any possible future damage in the surrounding area using ANN. Another study shows that ANN can be used to predict the water flow of dams that have much flood data, while regression models are better for dams that have limited flood data [10].…”

Section: B Why Evolutionary Computation Models?mentioning

confidence: 99%

“…Forecasting aims to offer a reliable guess about what could happen. River flow forecasting can help 1) the effective management of floods by delivering an early alert and permitting arrangements to be made to avoid damages [1] 2) assist in the supervision of water resources by offering data on the accessibility and timing of water supply to allow for better optimization of water allocation and guarantee that water resources are used effectively [2] 3) offer farmers with adequate data on the timing and amount of water accessibility, permitting them to plan their implanting and harvesting plans [3] 4) improve the supervision of hydropower generation by offering information on the likely flow of water, permitting power plants to be driven more economically [4], [5] and 5) the management of environmental matters, such as the safety of wetlands and fish habitats, so we may identify regions that require protection and plan healthy ecosystems [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

Sheta¹,

Abdel-Raouf²,

Fraihat³

et al. 2023

IJACSA

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The earth's population is growing at a rapid rate, while the availability of water resources remains limited. Water is required for various purposes, including drinking, agriculture, industry, recreation, and development. Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity. Developing precise river flow forecasting models can greatly improve the management of water resources in many countries. In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States. In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model. In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters. Fine-tuning the MG-GP parameters improves the prediction accuracy of the model. The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively.

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Section: B Why Evolutionary Computation Models?mentioning

confidence: 99%