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.
Many problems were encountered during the drilling operations in Zubair oilfield. Stuckpipe, wellbore instability, breakouts and washouts, which increased the critical limits problems, were observed in many wells in this field, therefore an extra non-productive time added to the total drilling time, which will lead to an extra cost spent. A 1D Mechanical Earth Model (1D MEM) was built to suggest many solutions to such types of problems. An overpressured zone is noticed and an alternative mud weigh window is predicted depending on the results of the 1D MEM. Results of this study are diagnosed and wellbore instability problems are predicted in an efficient way using the 1D MEM. Suitable alternative solutions are presented
... Show MoreIn the current study, haemoglobin analytes dissolved in a special buffer (KH2PO4(1M), K2HPO4(1M)) with pH of 7.4 were used to record absorption spectra measurements with a range of concentrations from (10-8 to 10-9) M and an absorption peak of 440nm using Broadband Cavity Enhanced Absorption Spectroscopy (BBCEAS) which is considered a simple, low cost, and robust setup. The principle work of this technique depends on the multiple reflections between the light source, which is represented by the Light Emitting Diode 3 W, and the detector, which is represented by the Avantes spectrophotomer. The optical cavity includes two high reflectivity ≥99% dielectric mirrors (dia
... Show MoreThe different interactions between cometary tail and solar wind ions are studied in the present paper based on three-dimensional Lax explicit method. The model used in this research is based on the continuity equations describing the cometary tail-solar wind interactions. Three dimensional system was considered in this paper. Simulation of the physical system was achieved using computer code written using Matlab 7.0. The parameters studied here assumed Halley comet type and include the particle density , the particles velocity v, the magnetic field strength B, dynamic pressure p and internal energy E. The results of the present research showed that the interaction near the cometary nucleus is mainly affected by the new ions added to the
... Show MoreLong memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify and estimate the long memory parameter in partially integrated time series. One of the most common models used to represent time series that have a long memory is the ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are a fractional number called the fractional parameter. To analyze and determine the ARFIMA model, the fractal parameter must be estimated. There are many methods for fractional parameter estimation. In this research, the estimation methods were divided into indirect methods, where the Hurst parameter is estimated fir
... Show MoreThe paper generates a geological model of a giant Middle East oil reservoir, the model constructed based on the field data of 161 wells. The main aim of the paper was to recognize the value of the reservoir to investigate the feasibility of working on the reservoir modeling prior to the final decision of the investment for further development of this oilfield. Well log, deviation survey, 2D/3D interpreted seismic structural maps, facies, and core test were utilized to construct the developed geological model based on comprehensive interpretation and correlation processes using the PETREL platform. The geological model mainly aims to estimate stock-tank oil initially in place of the reservoir. In addition, three scenarios were applie
... Show MoreThe convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog
... Show MoreAbstract
The government spending in Iraq and witnessed the changes and developments, especially after 2003, which outweighed consumer spending at the expense of capital expenditure and increased support and diversity of trends towards improving pension conditions for member
... Show MoreBackground: Low-level laser therapy (LLLT) has been extensively applied to improve wound healing due to some biostimulatory properties presented by laser arrays apparently able to accelerate the repair of soft tissue injuries. However, the role of proinflammatory interlukines not been studied yet. IL_1 ? represent one of the most important poroinflammatory interlukines that involved in wound healing. The goal of this study was to investigate the effect of 790-805nm diode laser on the expression of IL_1 ? during wound healing in mice. Materials and Methods: Standard-sized wounds (1.5cm) were carried out in the face of 96 white albino mice. Half of them underwent LLLT treatment (360 J/cm 2) at 790-805 nm delivered immediately after wound pro
... Show MoreE-learning is a necessity imposed by the Corona pandemic, which has disrupted various educational institutions in the world, but some of these institutions have not been affected and education has continued with them, due to their flexible educational system that was able to employ technology in the continuity of the educational process in the so-called e-learning, because It has characteristics that make it the most suitable alternative to avoid the consequences of the Corona pandemic and its damage to the educational process, as e-learning is one of the modern methods that contribute to enhancing the effectiveness of the learner, and enabling him to assume greater responsibility compared to traditional education, so the learner becomes
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