A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility

Enhanced sensitivity and detection of near-infrared refractive index sensor with plasmonic multilayers

In this work, the multilayer of the surface plasmon resonance (SPR) sensor was optimized to achieve the maximum sensor sensitivity. By optimizing the thickness of the silver layer (Ag) and dielectric films (TiO2 and AlAs), the optimum sensitivity of the SPR sensor could be obtained. The performance of the SPR sensor proposed was compared with control simulations utilizing zinc oxide (ZnO) and molybdenum oxide (MoO3 ). The numerical results indicate that the figure-of-merits (FOM) of the SPR sensor was achieved around 150/RIU, corresponding to the sensor sensitivity of 162.79◦/RIU with the optimized thicknesses of the TiO2, Ag, and AlAs layers of 140 nm, 60 nm, and 25 nm, respectively. This refractive index sensor shows the FOM to have high detection accuracy and high sensitivity that lead to finding potential application in bio-chemical detection with a small volume of liquid used in biological diagnosis

The Rotterdam Study: 2012 objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, oncological, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over a 1,000 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods