Unraveling the complexities of urban fluvial flood hydraulics through AI.

As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models are available for analyzing urban flooding; however, meeting the demand of high spatial extension and finer discretization and solving the physics-based numerical equations are computationally expensive. Computational efforts increase drastically with an increase in model dimension and resolution, preventing current solutions from fully realizing the data revolution. In this research, we demonstrate the effectiveness of artificial intelligence (AI), in particular, machine learning (ML) methods including the emerging deep learning (DL) to quantify urban flooding considering the lower part of Darby Creek, PA, USA. Training datasets comprise multiple geographic and urban hydraulic features (e.g., coordinates, elevation, water depth, flooded locations, discharge, average slope, and the impervious area within the contributing region, downstream distance from stormwater outfalls and dams). ML Classifiers such as logistic regression (LR), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN) are used to identify the flooded locations. A Deep neural network (DNN)-based regression model is used to quantify the water depth. The values of the evaluation matrices indicate satisfactory performance both for the classifiers and DNN model (F-1 scores- 0.975, 0.991, 0.892, and 0.855 for binary classifiers; root mean squared error- 0.027 for DNN regression). In addition, the blocked K-folds Cross Validation (CV) of ML classifiers in detecting flooded locations showed satisfactory performance with the average accuracy of 0.899, which validates the models to generalize to the unseen area. This approach is a significant step towards resolving the complexities of urban fluvial flooding with a large multi-dimensional dataset in a highly computationally efficient manner.

A Framework for Modeling Flood Depth Using a Hybrid of Hydraulics and Machine Learning.

Solving river engineering problems typically requires river flow characterization, including the prediction of flow depth, flow velocity, and flood extent. Hydraulic models use governing equations of the flow in motion (conservation of mass and momentum principles) to predict the flow characteristics. However, solving such equations can be substantially expensive, depending upon their spatial extension. 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. Such simulations are required for comprehensive river modeling, where a system of connected rivers is to be simulated simultaneously. 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. The aim of this paper is to present an efficient flood simulation framework that can be applied to large-scale simulations. The framework outlines a novel, quick, efficient and versatile model to identify flooded areas and the flood depth, using a hybrid of hydraulic model and ML measures. 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. The first ML model included a random forest (RF) classification model, which was used to identify wet or dry nodes over the domain. The second was a multilayer perceptron (MLP) model that was developed and trained by the iRIC simulation results, in order to estimate river depth in wet nodes. For the test data the overall accuracy of 98.5 percent was achieved for the RF classification. The regression coefficient for the MLP model for depth was 0.88. The framework outlined in this paper can be used to couple hydraulics and ML models to reduce the computation time, resources and expenses of large-scale, real-time simulations, specifically for two- or three-dimensional hydraulic modeling, where traditional hydraulic models are infeasible or prohibitively expensive.

Evaluation of Antioxidant Activity and Growth Control Properties of Nonoscale Structure Produced from Aloe vera var. littoralis Extract on Clinical Isolates of Salmonella.

The aim of the study was to examine antibacterial properties of microemulsion structure produced from Aloe vera var. littoralis extract as a new tool of nanoscale drug-like materials. Aloe vera var. littoralis (A. littoralis) extract was prepared by distillation method. A nonocarrier structure in the microemulsion system was prepared from the extract. Serial concentrations were prepared from 8 mg/mL extract and the nonocarrier containing 0.1 mg/mL pure extract and were evaluated by a disk diffusion method for 35 Salmonella clinical isolates. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined by microbroth dilution assay using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) method by an enzyme-linked immunosorbent assay(ELISA) Microplate Reader apparatus. Antioxidant activity of the extract was determined by measuring the ferric reducing ability of plasma (FRAP) assay. From 35 clinical isolates of Salmonella, 17 isolates-including resistant isolates of S.E.1103 and S.E.49-had a zone of inhibition (ZI) of 7 to 32 mm in 0.007 mg/mL of the extract. S.E.76 isolate exposed to 30 µg/mL ceftazidime disk had a ZI of 12 mm but had 10 mm in 7µg/mL of A. littoralis extract. The inhibitory effect of a nanocarrier at a concentration of 25 µg/mL by 20 mm ZI was comparable by the ceftazidime (30 µg/mL) effect. MIC50 was 0.25 mg/mL and MBC50 was 0.5 mg/mL by MTT method for the extract. It was shown that A.littoralis extract had antioxidant activity of 31.67 µM/mg that could be increased based on concentration. It was concluded that the nanocarrier had a significant effect on the studied isolates in comparison with ordinary antibiotics and had potential for use as a natural antioxidant and antimicrobial material in complementary medicine.

Transmission Electron Microscopy of XDR Mycobacterium tuberculosis Isolates Grown on High Dose of Ofloxacin.

The aim of the study was to investigate behavior of resistant Mycobacterium tuberculosis (MTB) isolates under a high dose of ofloxacin and its morphological changes. 19 extensively drug resistant (XDR) clinical isolates of MTB were grown on Löwenstein-Jensen medium containing progressively increasing concentrations of ofloxacin (2, 4, 8, 16, 32 mg/L). Ultra-structure analyses of resistant isolates grown on ofloxacin were conducted by transmission electron microscopy (TEM). Fixation was carried out by 4% glutaraldehyde in 0.1 M sodium cacodylate buffer on 300 mesh carbon formvar copper grid. The samples were negatively stained with uranium acetate suspension. All19XDRMTBisolatesweregrownandformedcoloniessuccessfullyon2,4,8mg/L,sevenisolates on16mg/L,andfourisolateson32mg/Lofloxacin. Morphologicalchangesandunusualformswere detected in 8, 16 and 32 mg/L ofloxacin at 43%, 76.5% and 81% of cells, respectively. Swollen form (protoplast like), ghost-like cell, degraded forms, and in a few cases, detached cytoplasm from cell wall were clearly detected in high drug concentrations in comparison to control. Changes in morphology were increased with increasing ofloxacin concentrations (p < 0.05). Some XDR isolates could be successfully grown on high doses of ofloxacin (32 mg/L), but with changes in morphology. It was concluded that several magnitudes of the drug doses could not prevent growth of drug resistant forms.