Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably.
In the present research, the electrical properties which included the ac-conductivity (σac), loss tangent of dielectric (tan δ) and real dielectric constant (ε’) are studied for nano polycarbonate in different pressures and frequencies as a function of temperature these properties were studied at selective temperature gradients which are (RT-50-100-150-250)°C. The results of the study showed that the values of dielectric constant and dissipation factor increase with increasing pressure and temperature and decreases by increasing frequency. And the results of electrical conductivity showed that it increases with increasing temperature, pressure and frequency.
An experimental investigation of the variation of argon discharge current with a glow and afterglow time intervals of a square discharge voltage was carried out at low pressure (6-11 mbar). The discharge was created between two circular metal electrodes of diameter (7.5 cm), separated horizontally by a distance (10 cm) at the two ends of a Pyrex cylindrical tube. A composite of two Gaussian functions has been suggested to fit and explain the variation graphs clearly. It is shown that the necessary times of glow and afterglow needed to attain a maximum discharge current are (70 us) and (60 us), respectively. The discharge current is observed to drop to the lowest value when the two times are serially longer than (85 us) and (72 u
... Show MoreIn this work, the plasma parameters (electron temperature and
electron density) were determined by optical emission spectroscopy
(OES) produced by the RF magnetron Zn plasma produced by
oxygen and argon at different working pressure. The spectrum was
recorded by spectrometer supplied with CCD camera, computer and
NIST standard of neutral and ionic lines of Zn, argon and oxygen.
The effects of pressure on plasma parameters were studied and a
comparison between the two gasses was made.
In our article, three iterative methods are performed to solve the nonlinear differential equations that represent the straight and radial fins affected by thermal conductivity. The iterative methods are the Daftardar-Jafari method namely (DJM), Temimi-Ansari method namely (TAM) and Banach contraction method namely (BCM) to get the approximate solutions. For comparison purposes, the numerical solutions were further achieved by using the fourth Runge-Kutta (RK4) method, Euler method and previous analytical methods that available in the literature. Moreover, the convergence of the proposed methods was discussed and proved. In addition, the maximum error remainder values are also evaluated which indicates that the propo
... Show MoreMarkov chains are an application of stochastic models in operation research, helping the analysis and optimization of processes with random events and transitions. The method that will be deployed to obtain the transient solution to a Markov chain problem is an important part of this process. The present paper introduces a novel Ordinary Differential Equation (ODE) approach to solve the Markov chain problem. The probability distribution of a continuous-time Markov chain with an infinitesimal generator at a given time is considered, which is a resulting solution of the Chapman-Kolmogorov differential equation. This study presents a one-step second-derivative method with better accuracy in solving the first-order Initial Value Problem
... Show MoreMaulticollinearity is a problem that always occurs when two or more predictor variables are correlated with each other. consist of the breach of one basic assumptions of the ordinary least squares method with biased estimates results, There are several methods which are proposed to handle this problem including the method To address a problem and method To address a problem , In this research a comparisons are employed between the biased method and unbiased method with Bayesian using Gamma distribution method addition to Ordinary Least Square metho
... Show MoreProjects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postpo