Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration

Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET0) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh) and wind speed (U2), from the related meteorological station are used in the prediction of ET0 values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions

Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study

The global solar radiation prediction is the most necessary part of the project and performance of solar energy applications. The objective of the present work is to predict global solar radiation (GSR) received on the horizontal surface using an artificial neural network (ANN). For the city (Relizane) in the west region of Algeria. The inputs used in the neural network are: time (h), day, month, year, temperature (k), relative humidity (%), pressure (mbar), wind speed (m/s), wind direction (°), and rainfall (kg/m2). The neural network-optimal model was trained and tested using 80 %, and 20 % of whole data, respectively. The best results were obtained with the structure 10-25-1 (10 inputs, 25 hidden, and 1 output neurons) presented an excellent agreement between the calculated and the experimental data during the test stage with a correlation coefficient of R = 0.9879, root means squared error of RMSE = 47.7192 (Wh/m2), mean absolute error MAE = 27.7397 (Wh/m2), and mean squared error MSE = 2.2771e+03(Wh/m2), considering a three-layer Feedforward neural network with Regularization Bayesienne (trainbr)  training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. The results demonstrate proper ANN’s predictions with a root mean square error (RMSE) of less than 0.50 (Wh/m2) and coefficient of correlation (R) higher than 0.98, which can be considered very acceptable. This model can be used for designing solar energy systems in the hottest regions

Driving IT projects to success: stakeholders’ importance: an artificial neural network model to demonstrate the potential of using stakeholder characteristics in IT projects’ success estimation

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, Specialization in Information Systems and Technology ManagementTechnology is all around, progressively present with each passing day. Companies recognize the usefulness of technology in business, leading to a growing number of Information Technology (IT) projects development. Due to its increasing scope, IT projects are getting more and more complex and expectations on their results are at an all-time high. At this rate, there is no telling where this complexity will lead, nor if expectations can be met. The development of IT project, or projects of any kind, is always met with unforeseen risks. Therefore, models that aim to estimate the success of these projects have been emerging. Some of these tools have fallen upon the bias of only taking into consideration a few project management variables for forecasting success. This may lead to inaccurate estimations, from the point-of-view of the several stakeholders. Considering the intricacy of IT projects, and the several aspects that influence them, advanced statistical models are required to give rich insight into projects’ outcome. On the other hand, project success cannot be fully determined if the stakeholders’ points-of-view are not taken into account. In other words, the success index of a project must be estimated having stakeholders taken into consideration. In order to support the mentioned concerns, a predictive model using Artificial Neural Networks was developed. Projects and stakeholders characteristics are defined, along with projects’ success criteria as inputs of the model, generating success indexes by budget, time and scope performance, as well as an overall success index as outputs. This dissertation adds to the current literature on the subject, by demonstrating the importance of stakeholder characteristics in project estimation and paving a pathway for the further exploration of the model developed. Thus making a first step into building a prediction tool to help mitigate the current risks of IT projects and software development

Neural Modeling and Imaging of the Cortical Interactions Underlying Syllable Production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production with and without jaw perturbations.National Institute on Deafness and Other Communication Disorders (R01 DC02852, RO1 DC01925

Towards Developing a Travel Time Forecasting Model for Location-Based Services: a Review

Travel time forecasting models have been studied intensively as a subject of Intelligent Transportation Systems (ITS), particularly in the topics of advanced traffic management systems (ATMS), advanced traveler information systems (ATIS), and commercial vehicle operations (CVO). While the concept of travel time forecasting is relatively simple, it involves a notably complicated task of implementing even a simple model. Thus, existing forecasting models are diverse in their original formulations, including mathematical optimizations, computer simulations, statistics, and artificial intelligence. A comprehensive literature review, therefore, would assist in formulating a more reliable travel time forecasting model. On the other hand, geographic information systems (GIS) technologies primarily provide the capability of spatial and network database management, as well as technology management. Thus, GIS could support travel time forecasting in various ways by providing useful functions to both the managers in transportation management and information centers (TMICs) and the external users. Thus, in developing a travel time forecasting model, GIS could play important roles in the management of real-time and historical traffic data, the integration of multiple subsystems, and the assistance of information management. The purpose of this paper is to review various models and technologies that have been used for developing a travel time forecasting model with geographic information systems (GIS) technologies. Reviewed forecasting models in this paper include historical profile approaches, time series models, nonparametric regression models, traffic simulations, dynamic traffic assignment models, and neural networks. The potential roles and functions of GIS in travel time forecasting are also discussed.

A Multi-Contextual Approach to Modeling the Impact of Critical Highway Work Zones in Large Urban Corridors

Accurate Construction Work Zone (CWZ) impact assessments of unprecedented travel inconvenience to the general public are required for all federally-funded highway infrastructure improvement projects. These assessments are critical, but they are also very difficult to perform. Most existing prediction approaches are project-specific, shortterm, and univariate, thus incapable of benchmarking the potential traffic impact of CWZs for highway construction projects. This study fills these gaps by creating a big-data-based decision-support framework and testing if it can reliably predict the potential impact of a CWZ under arbitrary lane closure scenarios. This study proposes a big-data-based decision-support analytical framework, “Multi-contextual learning for the Impact of Critical Urban highway work Zones” (MICUZ). MICUZ is unique as it models the impact of CWZ operations through a multi-contextual quantitative method utilizing sensored big transportation data. MICUZ was developed through a three-phase modeling process. First, robustness of the collected sensored data was examined through a Wheeler’s repeatability and reproducibility analysis, for the purpose of verifying the homogeneity of the variability of traffic flow data. The analysis results led to a notable conclusion that the proposed framework is feasible due to the relative simplicity and periodicity of highway traffic profiles. Second, a machine-learning algorithm using a Feedforward Neural Networks (FNN) technique was applied to model the multi-contextual aspects of iii long-term traffic flow predictions. The validation study showed that the proposed multi-contextual FNN yields an accurate prediction rate of traffic flow rates and truck percentages. Third, employing these predicted traffic parameters, a curve-fitting modeling technique was implemented to quantify the impact of what-if lane closures on the overall traffic flow. The robustness of the proposed curve-fitting models was then scientifically verified and validated by measuring forecast accuracy. The results of this study convey the fact that MICUZ would recognize how stereotypical regional traffic patterns react to existing CWZs and lane closure tactics, and quantify the probable but reliable travel time delays at CWZs in heavily trafficked urban cores. The proposed framework provides a rigorous theoretical basis for comparatively analyzing what-if construction scenarios, enabling engineers and planners to choose the most efficient transportation management plans much more quickly and accurately

VGM-RNN: Recurrent Neural Networks for Video Game Music Generation

The recent explosion of interest in deep neural networks has affected and in some cases reinvigorated work in fields as diverse as natural language processing, image recognition, speech recognition and many more. For sequence learning tasks, recurrent neural networks and in particular LSTM-based networks have shown promising results. Recently there has been interest – for example in the research by Google’s Magenta team – in applying so-called “language modeling” recurrent neural networks to musical tasks, including for the automatic generation of original music. In this work we demonstrate our own LSTM-based music language modeling recurrent network. We show that it is able to learn musical features from a MIDI dataset and generate output that is musically interesting while demonstrating features of melody, harmony and rhythm. We source our dataset from VGMusic.com, a collection of user-submitted MIDI transcriptions of video game songs, and attempt to generate output which emulates this kind of music

elopment of Neural Network Model for Predicting Crucial Product Properties or Yield for Optimisation of Refinery Operation

Refinery optimisation requires accurate prediction of crucial product properties and yield of desired products. Neural network modeling is an alternative approach to prediction using mathematical correlations. The project is an extension of a previous research conducted by the university on product yield and properties prediction using non-linear regression method. The objectives of this project are to develop a framework for the application of neural network modeling in predicting refinery product yield and properties, to develop neural network model for three case studies (predicting crude distillation yield, diesel pour point and hydrocracker total gasoline yield) and to evaluate the suitability of using neural networkmodelingfor predicting refinery product yield and properties. The project methodologies used are literature research and computer modeling using MATLAB neural network toolbox. The framework development for neural network modeling include aspects such as process understanding, data collection and division, input elements selection, data preprocessing, network type selection, design of network architecture, learning algorithm selection, network training, and network simulation using new data set. Various configurations of neural network model were tested to choose the best model to represent each case study. The model selected has the smallestmean squared error when simulated using test data. The results are presented in the form of the network configuration that gives the smallest MSE, plots comparing the actual output with the output predictedby the neural network, as well as residual analysis results to determine the range of deviationbetween the actual and predicted output. Although the accuracy of the output predicted by the neural network model requires further improvement, in general, the study has shown the tremendous potential for the use of neural networkfor predicting refinery product yield and properties. Suggestions for future study in the area include improvement of the model accuracy using advanced methods such as cross-training and stacked network, integration of neural networkwith plant's Advanced Process Control as inferential property predictor, and study on inverted network for use in a neural network-based controller

A Review: Effort Estimation Model for Scrum Projects using Supervised Learning

Effort estimation practice in Agile is a critical component of the methodology to help cross-functional teams to plan and prioritize their work. Agile approaches have emerged in recent years as a more adaptable means of creating software projects because they consistently produce a workable end product that is developed progressively, preventing projects from failing entirely. Agile software development enables teams to collaborate directly with clients and swiftly adjust to changing requirements. This produces a result that is distinct, gradual, and targeted. It has been noted that the present Scrum estimate approach heavily relies on historical data from previous projects and expert opinion, while existing agile estimation methods like analogy and planning poker become unpredictable in the absence of historical data and experts. User Stories are used to estimate effort in the Agile approach, which has been adopted by 60–70% of the software businesses. This study's goal is to review a variety of strategies and techniques that will be used to gauge and forecast effort. Additionally, the supervised machine learning method most suited for predictive analysis is reviewed in this paper