Towards better flood risk management using a Bayesian network approach

Towards better flood risk management using a Bayesian network approach

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After years of drought, the rainy season is always welcomed.Unfortunately, this can also herald widespread flooding which can result in loss AEG DEK431010M SurroundCook Built-in Double Oven with Programmable Timer of livelihood, property, and human life.In this study a Bayesian network is used to develop a flood prediction model for a Tshwane catchment area prone to flash floods.This causal model was considered due to a shortage of flood data.The developed Bayesian network was evaluated by environmental domain experts and implemented in Python through pyAgrum.

Three what-if scenarios are used to verify the model and estimation of probabilities which were based on expert knowledge.The model was then used to predict a low and high rainfall scenario.It was able to predict no flooding events for a low rainfall scenario, and flooding events, Niacin / Niacinamide especially around the rivers, for a high rainfall scenario.The model therefore behaves as expected.