A novel methodology to predict monthly municipal water demand based on weather variables scenario

This study provides a novel methodology to predict monthly water demand based on several weather variables scenarios by using combined techniques including discrete wavelet transform, principal component analysis, and particle swarm optimisation. To our knowledge, the adopted approach is the first technique to be proposed and applied in the water demand prediction. Compared to traditional methods, the developed methodology is superior in terms of predictive accuracy and runtime. Water consumption coupled with weather variables of the Melbourne City, from 2006 to 2015, were obtained from the South East Water retail company. The results showed that using data pre-processing techniques can significantly improve the quality of data and to select the best model input scenario. Additionally, it was noticed that the particle swarm optimisation algorithm accurately predicts the constants of the suggested model. Furthermore, the results confirmed that the proposed methodology accurately estimated the monthly data of municipal water demand based on a range of statistical criteria

Modeling Unobserved Heterogeneity and the Injury Severities of Truck Drivers in Run-Off-Road (ROR) Crashes: Econometric Methods and Applications

Recent statistics regarding large truck crashes reveal that fatality rates of large trucks per 100 million vehicle miles traveled (VMT) and fatality rates per 1,000 registered vehicles are higher than those for passenger vehicles. These statistics underscore the need for greater efforts by safety professionals to help mitigate the impacts of these types of crashes on society and on the ground freight transportation industry as a whole. Of special interest are run-off-road (ROR) crashes (also referred to as roadway departure), which are crashes that occur due to a vehicle crossing an edge line or a center line of a roadway or/and leaving the designated lane. These types of crashes roughly constituted 54% of all traffic fatalities in the U.S. for the period between 2013 and 2015. There have been several efforts that have addressed large truck-involved crashes from varying perspectives (e.g., time of day, facility type, and single vs. multivehicle, work zone). However, there are several research gaps that need further attention, for example, better understanding of the relationship between contributing factors and driver injury severities due to ROR crashes involving large trucks. Specifically, those driver injury severities related to the impact of lighting conditions (dark vs. lighted) and land use (urban vs. rural). From a methodological modeling perspective accounting for unobserved heterogeneity has traditionally been ignored in regard to ROR driver injury severity analyses. To address these gaps, this dissertation aims to develop and estimate advanced econometric models that account for unobserved heterogeneity to better understand the contributing factors of driver injury severity of ROR crashes involving large trucks in the state of Oregon. This dissertation includes three manuscripts that investigate injury severity of large truck drivers involved in ROR crashes in the state of Oregon for the period 2007 to 2014. In the first manuscript, an ordered random parameter probit model was estimated to predict the likelihood of three injury severity categories using Oregon crash data: severe injury (fatal and incapacitating), minor injury (non-incapacitating and possible injury), and no injury while addressing the unobserved heterogeneity. The modeling framework presented in this manuscript offers a flexible methodology to analyze ROR crashes involving large trucks while accounting for unobserved heterogeneity. The second manuscript examines the impact of lighting conditions on injury severity of large truck drivers involved in ROR crashes. This was done by disaggregating crash data by lighting conditions into two datasets: one for the lighted condition and the other for the dark condition. Hence, two separate mixed logit models were developed to capture the contributing factors that affect injury severity in each lighting condition while accounting for unobserved heterogeneity. To validate the estimation results, series of likelihood ratio tests were conducted. Model separation tests along with estimation results indicate that lighting conditions need to be analyzed separately with 99.99% confidence. Lastly, an in-depth analysis was conducted in the third manuscript to examine the effect of land use setting (urban vs. rural) on injury severity of large truck drivers involved in ROR crashes. Again, disaggregating crash data was achieved to create two independent datasets: one pertaining to ROR crashes involving large trucks occurring on urban and the other for those occurring on rural areas. Instead of utilizing random parameter approach as a framework to account for unobserved heterogeneity, this manuscript utilizes two latent class ordered probit models to capture factors exclusively that contribute to each land use type. Once again, model separation tests along with estimation results reveal that there are distinctions in terms of contributing factors based on land use type. Therefore, ROR crashes involving large trucks need to be analyzed separately based on land use with 99.99% confidence. It is expected that estimating advanced econometric methods to identify contributing factors to injury severity of large truck drivers involved in ROR crashes while accounting for unobserved heterogeneity can be used as a basis to aid transportation safety engineers, trucking industry, transportation planners, and state agencies in implementing appropriate safety countermeasures to help mitigate ROR crashes. In terms of study implications, the estimation results of this dissertation have direct implications on safety policy. For instance, the finding of this dissertation reinforces the notion that the current policy regarding seatbelt usage should be amended in an attempt to increase seatbelt usage by penalizing drivers violating this policy to deterrent fines and forcing them to enroll in a required defensive driving course in the state of Oregon

The Role of Management in Preserving Documents in Iraqi Legislation: A Comparative Study

            تعد الوثائق على اختلاف أشكالها وأنواعها ذاكرة الأمة والأداة الأساسية في إثبات الحقوق والمصدر الأوّل للبحث العلمي والتاريخي، لذا كان الاهتمام بحفظها من المهام الأولية للمؤسسات العامة، عن طريق توافر الظروف المناسبة والملائمة لها وحمايتها وصيانتها وبثها وإيصالها إلى المستفيدين عبر الوسائل والتقنيات التي استخدمت عبر الزمن؛ لحفظ نسخ لهذه الوثائق، ومن أهم هذه الوسائل؛ التصوير المصغر، والحفظ بالصيغ الإلكترونية والرقمية التي تقدم إمكانيات كبيرة في الحفظ وخاصة في اختزال مكان الحفظ وإيصال البيانات والمعلومات للمستفيد، وإن الهدف من هذه الدراسة هو التعرّف على الواقع الحالي لحفظ الوثائق، ومعرفة الأنشطة التي تمارسها الإدارة في حفظ الوثائق والوقوف على مظاهر القصور والمشاكل التي تواجهها ومدى تأثير ذلك على أداء الإدارة.          Documents are of different forms and types of nations memory and the basic tool in proving the rights and the main sauce of scientific and historical research, so the concern for keeping it is one of the providing the right conditions for consecration protection maintenance broadcasting and delivery to beneficiates through the means and techniques used over time in order to sari copies of these documents the most important ones are microphotography and preservation in electronic and digital formats  which offers greasy possibilities in conservation especially in reducing the place of preservation and delivering data and information to the ben efficacy, the purpose of this study is to document preservation, and know the activities practiced by the administration the preservation of documents and to identify the shortcomings and problems the face and the extent to which this affects the performance .&nbsp

The Impact of Corporate Governance and Supply Chain Management on the Accounting and Auditing Environment

Abstract- The purpose of this study is to investigate the effect of Corporate Governance and Supply Chain Management on the Accounting and Auditing Environment. The spatial scope of the research includes companies which are admitted to the Tehran Stock Exchange. This research is based on objective, applied and descriptive method, and the realm of time is between 2011 and 2016. Research includes two independent variables (supply chain management and corporate governance), two dependent variable (accounting and auditing environment), and control variables. Information is collected in both library and field. Assumptions were tested through linear regression. The results of the test showed that corporate governance and supply chain management affects the accounting and auditing environment. Keywords: Auditing Environment, Corporate Governance, supply chain management, Accounting, Tehran Stock Exchange

Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-processing

Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models

A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach

Accurate and reliable urban water demand prediction is imperative for providing the basis to design, operate, and manage water system, especially under the scarcity of the natural water resources. A new methodology combining discrete wavelet transform (DWT) with an adaptive neuro-fuzzy inference system (ANFIS) is proposed to predict monthly urban water demand based on several intervals of historical water consumption. This ANFIS model is evaluated against a hybrid crow search algorithm and artificial neural network (CSA-ANN), since these methods have been successfully used recently to tackle a range of engineering optimization problems. The study outcomes reveal that 1) data preprocessing is essential for denoising raw time series and choosing the model inputs to render the highest model performance; 2) both methodologies, ANFIS and CSA-ANN, are statistically equivalent and capable of accurately predicting monthly urban water demand with high accuracy based on several statistical metric measures such as coefficient of efficiency (0.974, 0.971, respectively). This study could help policymakers to manage extensions of urban water system in response to the increasing demand with low risk related to a decision

Forecasting of Air Maximum Temperature on Monthly Basis Using Singular Spectrum Analysis and Linear Autoregressive Model

In this research, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature. The temperature time series is decomposed into three components and the trend component is subjected for modelling. The performance of modelling for both prediction and forecasting is evaluated via various model fitness function. The results show that the current method presents an excellent performance in expecting the maximum air temperature in future based on previous recordings

Genetic variation of ten Iraqi wheat genotypes using (SSR) markers and morphological characterization

The current study based on using morphological traits and simple sequence repeat(SSRs) markers to study variation among ten Iraqi wheat genotypes.  Primers  wmc596 and  wmc603 produced  three alleles distributed between one in wmc596 and two in wmc603 with an average number of 1.50 allele  per locus . Primer wmc603 was more  informative  than wmc596 as produced PIC reached  0.3750. Morphological traits including whole plant ( dry weight , height ,leaf number ,leaf area and branches number) , spike( dry weight ,length and number) and weight of 100 grain used for cluster analysis .Cluster analysis depending on  morphological traits grouped wheat genotypes among  two major groups , the first included only Faris genotype while the other large one included the rest genotypes which further divided in to two sub clusters. Genotypes  identification  and studying genetic variation produce an efficient tool for genotypes selecting in breeding programs

Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model

This paper investigates the autoregressive (AR) model performance in prediction and forecasting the monthly maximum temperature. The temperature recordings are collected over 12 years (i.e., 144 monthly readings). All the data are stationaries, which is converted to be stationary, via obtaining the normal logarithm values. The recordings are then divided into 70% training and 30% testing sample. The training sample is used for determining the structure of the AR model while the testing sample is used for validating the obtained model in forecasting performance. A wide range of model order is selected and the most suitable order is selected in terms of the highest modelling accuracy. The study shows that the monthly maximum temperature can accurately be predicted and forecasted using the AR model

Assessing the Benefits of Nature-Inspired Algorithms for the Parameterisation of ANN in the Prediction of Water Demand

Accurate forecasting techniques for a stochastic pattern of water demand are essential for any city that faces high variability in climate factors and a shortage of water resources. This is the first research that assesses the impact of climatic factors on urban water demand in Iraq, which is one of the hottest countries in the world. We present a novel forecasting methodology that includes data preprocessing and an artificial neural network (ANN) model, which is integrated by a recently nature-inspired metaheuristic algorithm (marine predators algorithm (MPA)). The MPA-ANN algorithm will be compared with four different nature-inspired metaheuristic algorithms. Nine climatic factors were examined with different scenarios to simulate the monthly stochastic urban water demand over eleven years for Baghdad City, Iraq. The results reveal that: 1) precipitation, solar radiation, and dew point temperature are the most relevant factors to develop the models. 2) The ANN model becomes more accurate when it is used in combination with the MPA. 3) This methodology can accurately forecast the water demand considering the variability in climatic factors. These findings are of considerable significance to water utilities to plan, review, and compare the availability of freshwater resources and increase water requests (i.e., adaptation variability of climatic factors)