An ideal classifier is one that can present high precision and high sensitivity for all classesThe closer the F1-score to a value of one, which is considered the maximum, demonstrates the best discrimination between two sets of samplesIn classification problems, one of the most common ways to describe the performance of a classifier is the confusion matrix.

H.H. The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Then, they assigned the remaining POT floods (1,223 in the West, 2,183 in Central, and 1,467 in the East) into the groups created by the record floods. The capability of the ANN to understand the behavior of a system is embedded in the weights of the connections between neurons. As a result, the MLP model structure was set to four hidden layers with 150, 100, 50, and 30 neurons respectively. Consequently, there is a great need for quick, robust, and versatile models for large-scale, real-time flood modeling. Therefore, the error in estimated WSE would essentially be the same as the error associated with predicted depth.Large floods are expected to occur more frequently around the globe due to global warming, which demands a new paradigm of robust, efficient, and real-time flood modeling. The International River Interface Cooperative (iRIC) is a two-dimensional hydraulic model with an integrated simulation solver. The activation function for all layers was Tansig (Eq. ; H.H.

The test resulted in an accuracy of 98.5%.The performance of a binary classifier can be measured by multiple conventional statistical indices such as F1-score, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The calibration of hydraulic models usually includes setting up the input variables (mainly the roughness coefficient) so that the model can accurately generate a measured water surface profile for a known discharge. Yitian, L. & Gu, R. R. Modeling flow and sediment transport in a river system using an artificial neural network. However, solving such equations can be substantially expensive, depending upon their spatial extension.

Moreover, True Positive (TP), False Negative (FN), True Negative (TN), and False Positive (FP) indices show the performance of the model in quantitative termsThe sensitivity shows the ability of the model to label all the positive samples correctly, and the precision shows the ability of the model not to label a negative sample as positiveFor any specific class, if the classifier presents high precision and low sensitivity, it means that the classifier is very conservative. While an ML classifier can distinguish between flooded and not flooded areas (wet and dry) over the domain with flow fluctuations, a regressor function can be used to estimate the depth of the flow in wet areas. Machine Learning (ML) approaches have shown promise for different water resources problems, and they have demonstrated an ability to learn from current data to predict new scenarios, which can enhance the understanding of the systems. To accomplish that, a two-dimensional hydraulic model (iRIC), calibrated by measured water surface elevation data, was used to train two ML models to predict river depth over the domain for an arbitrary discharge. Floodly’s rapid predictions complement traditional hydraulic modelling, which can be slower and more costly to apply. Helping deliver a flexible, integrated solution for water transfer and distribution until 2050

Furthermore, the application of ML in flood modeling is in its early stages and needs substantial improvementThe study area used for this investigation is a 3.5 kilometers (km) segment of the Green River located at 120 km downstream of the Flaming Gorge Dam in the northeast corner of Utah, with the river width varying from 100 to 150 m (Fig. The framework demonstrates how hydraulic and data-driven models can be used to (a) identify potentially flooded areas and (b) to estimate the probable depth of flooding in such areas. Click to open search The input data to the model includes geographic data and measured hydraulic data (mainly water surface elevation) for model calibration. Figure was created in iRIC version 3.0.18, revision 6257 (Comparison between measured and simulated water surface elevation along the river in the study area for a discharge of 247 mThe historic peak discharges of the river from 1951 to 2018 were obtained from the USGS gage located downstream of the Flaming Gorge Dam (USGS 09234500 Green River near Greendale, UT). The aim of this paper is to present an efficient flood simulation framework that can be applied to large-scale simulations. Moreover, modeling two- or three-dimensional river flows with high-resolution topographic data for large-scale regions (national or continental scale) is next to impossible. Share on Linkedin - Warning, this link will open a new tab. Smith, V. B.

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