Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

     

Characterization of the Ion Beam Current Density of the RF Ion Source with Flat and Convex Extraction Systems

Silicon - The characterization of ion beam current density distribution and beam uniformity is crucial for improving broad-beam ion source technologies. The design of the broad ion beam extraction...     

Influence of Ni Interlayers on the Mechanical Properties of Ti6Al4V/(WC-Co) Friction Welds

Ni interlayers were introduced prior to dissimilar friction welding of Ti6Al4V base material to three cemented carbide substrates. The fracture strength of Ti6Al4V/(WC-6 wt% Co) welds were poor and were markedly improved when 20-µm thick Ni interlayers were introduced prior to dissimilar friction welding. These results were only produced when the (WC-6 wt% Co) cermet was electroplated prior to friction welding. When the Ti6Al4V alloy was electroplated prior to friction welding, fractured WC particles and cracking were observed in the (WC-Co) carbide substrate. The fracture strengths of Ti6Al4V/(WC-11 wt% Co) and Ti6Al4V/(WC-24 wt% Co) welds were not improved when 20-µm thick Ni interlayers were introduced prior to friction welding. During mechanical testing, the Ni layer retained at the dissimilar joint interface created a region of weakness.     

Statistical analysis of fiber-optic CDMA communication systems. I. Device modeling

In this paper, we present a new methodology for analysis of all-optical fiber-optic code-division multiple-access (FO-CDMA) networks. In this analysis, we propose statistical models, based on photon-counting techniques, for some basic elements of the network, such as splitters, combiners, star couplers, and FO-CDMA passive encoders/decoders. By following the statistical variation of the photon-count of the string of pulses that constitutes the address sequence in an FO-CDMA network, we will be able to reveal the quantum-limited optical signal-to-noise ratio (OSNR) required at the transmitter output to meet the performance limits. Moreover, considering receiver thermal noise and source extinction ratio, we explore the role of using an optical preamplifier before the detector and the dependence of the performance on the transmitted power and the weight of the employed optical orthogonal codes (OOCs).     

Electro-optical characteristics of indium tin oxide (ITO) films: effect of thermal annealing

The effect of thermal annealing on the electrical and optical characteristics of ITO films prepared by reactive sputtering and thermal evaporation have been studied. The effect of the thermal annealing is to improve the conductivity and the optical transmission in the shorter wavelength region. The conductivity of the films increases with annealing temperature, this behaviour is associated with grain growth in the film.     

Low‐velocity impact of nanocomposite and polymer plates

Nanocomposites are more widely studied today because of higher stiffness, decreased permeability, thermal stability, and many other properties superior to those of regular polymers. However, manufacturers are concerned about implementing nanocomposites because of their lower impact properties with respect to the base polymer. This study focused on low‐velocity impact tests of a thermoplastic olefin by itself and with 5 wt % nanoclay. The impact tests were conducted at ?40, 23.9, and 65.6°C until the polymer and nanocomposite plates experienced complete striker penetration. The force–time and force–deflection responses obtained from the impact testing provided a means of comparing the impact performances of the two materials. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 2309–2315, 2005     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Using AR,MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data

Precipitation is one of the most important components of the hydrologic cycle as it is required for multi-objective applications including flood estimation, drought monitoring, watersheds management, hydrology, agriculture, etc. Therefore, its estimation and modeling via a suitable method is a challenging task for hydrologists. The present study seeks to model monthly precipitation at two stations located in Iran. Two artificial intelligence (AI)-based models consisting of multivariate adaptive regression splines (MARS) and k-nearest neighbors (KNN) were used as the modeling techniques. In doing so, nine single-input scenarios under limited climatic data are implemented using minimum, maximum, and mean air temperatures, dew point temperature, station pressure, vapor pressure, relative humidity, wind speed, and antecedent precipitation data. The attained results illustrate that the performance of single MARS and KNN is relatively poor when modeling the monthly precipitation. Additionally, this study develops hybrid models to enhance the precipitation modeling through combining the MARS and KNN models with three diverse types of the time series (TS) models, namely autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The most important justification for integrating the models applied is that the AI and TS-based models are respectively capable of modeling the non-linear and linear terms of the hydrological variables such as precipitation. It is therefore necessary to be considered both of the aforementioned terms in the modeling procedure. A performance comparison of the single and hybrid models denotes the higher accuracy of hybrid models than the single ones. However, the hybrid models generated by combining the KNN and the TS models used are the best-performing models.