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 this paper we estimate the coefficients and scale parameter in linear regression model depending on the residuals are of type 1 of extreme value distribution for the largest values . This can be regard as an improvement for the studies with the smallest values . We study two estimation methods ( OLS & MLE ) where we resort to Newton – Raphson (NR) and Fisher Scoring methods to get MLE estimate because the difficulty of using the usual approach with MLE . The relative efficiency criterion is considered beside to the statistical inference procedures for the extreme value regression model of type 1 for largest values . Confidence interval , hypothesis testing for both scale parameter and regression coefficients
... Show MoreThe current study aimed to use some bacterial isolates from the local soil of Baghdad city by study the effects of temperature, pH and incubation period on the growth rates of isolated bacteria and choose the optimal conditions for their diversity and for understanding bacterial growth and their requirements for survival and proliferation. This information can be applied to obtain their high growth rate for use in various fields such as agriculture, medicine and environmental sciences in the future. And it used to assess the degree of variation in across bacteria species in pH, temperature and incubation period. A number of local bacterial isolates as
This study utilized low-cost agricultural waste (molasses production waste powder) to extract copper ions from aqueous solutions. The present investigation explored a range of factors that influence the adsorption process, including temperature, pH, ionic strength, contact time, quantity of adsorbent, and particle size. Spectrophotometric analysis was used to determine the solution's absorbance both before and after the adsorption procedure. The Langmuir and Freundlich adsorption models were used to match the equilibrium data. The Freundlich model was determined to be the best isotherm model using the linear regression coefficient R2=0.9868. Thermodynamic parameters, including enthalpy, entropy, and Gibbs free energy, were calculate
... Show MoreThe shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial
... Show MoreBackground/Objectives: The purpose of current research aims to a modified image representation framework for Content-Based Image Retrieval (CBIR) through gray scale input image, Zernike Moments (ZMs) properties, Local Binary Pattern (LBP), Y Color Space, Slantlet Transform (SLT), and Discrete Wavelet Transform (DWT). Methods/Statistical analysis: This study surveyed and analysed three standard datasets WANG V1.0, WANG V2.0, and Caltech 101. The features an image of objects in this sets that belong to 101 classes-with approximately 40-800 images for every category. The suggested infrastructure within the study seeks to present a description and operationalization of the CBIR system through automated attribute extraction system premised on CN
... Show MoreImproved Merging Multi Convolutional Neural Networks Framework of Image Indexing and Retrieval
Herein, we report designing a new Δ (delta‐shaped) proton sponge base of 4,12‐dihydrogen‐4,8,12‐triazatriangulene (compound
Atrial fibrillation is associates with elevated risk of stroke. The simplest stroke risk assessment schemes are CHADS2 and CHA2DS2-VASc score. Aspirin and oral anticoagulants are recommended for stroke prevention in such patients.
The aim of this study was to assess status of CHADS2 and CHA2DS2-VASc scores in Iraqi atrial fibrillation patients and to report current status of stroke prevention in these patients with either warfarin or aspirin in relation to these scores.
This prospective cross-sectional study was carried out at Tikrit, Samarra, Sharqat, Baquba, and AL-Numaan hospitals from July 2017 to October 2017. CHADS2
... Show MoreThis study tries to clear the correlation and association between asthma, obesity and leptin levels. Also it will work to indicate the main risk factors which play role in the elevation of leptin level within asthmatic patients. This is a case control study conducted on (38) asthmatic patients and (20) healthy control who were closely similar by age, gender and BMI. The main statistical tests used were student t test, linear regression test and correlation test. Significance was set at P < 0.05. Sampling method used for this study was convenience sampling method. The main results of this study show a significant association and positive correlation between age (old age ≥ 40 ye
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