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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of phy...
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Context 1
... principle behind the ML modeling workflow and the strategy for flood modeling are described in detail in the literature [48,65]. Figure 2 represents the basic flow for building an ML model. The major ML algorithms applied to flood prediction include ANNs [66], neuro-fuzzy [67], adaptive neuro-fuzzy inference systems (ANFIS) [68], support vector machines (SVM) [69], wavelet neural networks (WNN) [70], and multilayer perceptron (MLP) [71]. ...
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Extreme precipitation can have catastrophic effects in China by triggering floods, landslides, and other natural disasters. We measure extreme precipitation over eastern China by the seasonal maximum of total precipitation over 5 consecutive days (Rx5day) in June, July, and August (JJA), which contributes more than 20% of the climate mean of JJA re...
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of phy...
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of ph...
Citations
... Artificial neural networks (ANNs) are one of the most widely used algorithms for predicting both short-and long-term hydrological variables such as river flow discharge and flood levels (Maier and Dandy, 2000;Fahimi et al., 2017;Dawson and Wilby, 2001;Kim and Barros, 2001). Other commonly used algorithms include decision trees, support vector machines, and ensemble ML models such as random forest (Mosavi et al., 2018). ...
In the last few decades, remote sensing has played a major role in flood risk management strategies across the world. Geomatics products have been used to identify flood extents and impacts, to inform flood and evacuation models, and as part of emergency response activities. Recent technological advances have provided an opportunity to explore the use of remote sensing further within the context of flood risk management. For example, unmanned aircraft systems have been used to model evacuation routes and post-event to assess the impact of flooding at property level, identify households that would benefit from resilient and resistance measures, and identify the source of flooding. This chapter describes current applications of remote sensing within flood risk management and flood emergency response. The first section discusses the application of remote sensing in flood modeling, flood damage assessment, and vulnerability. The second section reviews how the development of remote sensing technologies has extended the range of their application in flood risk management. The last section captures key considerations for the use of standardized remote sensing data collection approaches to inform flood risk management activities.
Natural Disasters like cyclones and Earthquakes have a huge impact on the lives of people, results in damage to infrastructure, and lead to injuries and deaths. IoT Based detection systems are utilized for detecting disasters and performing subsequent rescue operations. The challenge with these IoT Based systems is that collecting data from sensors might be failed due to communication breakages or network congestions. To address this issue, this paper has come up with an idea of implementing Disaster Detection using Convolutional Neural Networks and sending SMS to people for making people alert. This paper aims to particularly detect Cyclones and Earthquakes. Data sets were collected from Kaggle. Convolutional Neural Network is a deep learning algorithm that takes an image as input, assigns weights/biases to a variety of aspects in the image for differentiating one from another image. Applications of this work includes disaster preparedness such as forecasts, warnings and predictions, disaster management and disaster relief operations. A comparative study has been performed on CNN and its variants.
The aim of this study was to develop hydrological models that can represent different geo-climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and semi-humid areas suffer from frequent flood events, whereas semi-arid areas suffer from flash floods because of urbanization and climate change, which contribute to an increase in runoff. This study applied ɛ-Support Vector Machine (ε-SVM) and artificial neural network (ANN) for the simulation and forecasting streamflow of three different catchments. The Evolutionary Strategy (ES) optimization method was used to optimize the ANN and SVM sensitive parameters. The relative performance of the two models was compared, and the results indicate that both models performed well for humid and semi-humid systems, and SVM generally perform better than ANN in the streamflow simulation of all catchments.
