Search results for: Water demand forecast; Neural Networks model; water resources management; Saudi Arabia.

Authors: Amir Reza Saffari Azar Alamdari

Abstract:

In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot-s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervised learning. A modified A* algorithm is used to find the best path using this internal representation between starting and goal points. This method can be used with good performance for both known and unknown environments.

Keywords: Mobile Robot, Path Planning, Self-organization, Spiking Neural Networks.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1468

Authors: G. A. Adamu, M. S. Sallau, S. O. Idris, E. B. Agbaji

Abstract:

Drinking water is supplied to Danbatta, Makoda and some parts of Minjibir local government areas of Kano State from the surface water of Thomas Reservoir. The present land use in the catchment area of the reservoir indicates high agricultural activities, fishing, as well as domestic and small scale industrial activities. To study and monitor the quality of surface and drinking water of the area, water samples were collected from the reservoir, treated water at the treatment plant and potable water at the consumer end in three seasons November - February (cold season), March - June (dry season) and July - September (rainy season). The samples were analyzed for physical and chemical parameters, pH, temperature, total dissolved solids (TDS), conductivity, turbidity, total hardness, suspended solids, total solids, colour, dissolved oxygen (DO), biological oxygen demand (BOD), chloride ion (Cl^-) nitrite (NO₂^-), nitrate (NO₃^-), chemical oxygen demand (COD) and phosphate (PO₄^3-). The higher values obtained in some parameters with respect to the acceptable standard set by World Health Organization (WHO) and Nigerian Industrial Standards (NIS) indicate the pollution of both the surface and drinking water. These pollutants were observed to have a negative impact on water quality in terms of eutrophication, largely due to anthropogenic activities in the watershed.

Keywords: Surface water, drinking water, water quality, pollution, Thomas reservoir, Kano.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1503

Authors: Frantisek Bozek, Alena Bumbova, Eduard Bakos

Abstract:

Paper is dealing with vulnerability concerning elements of hydrological structures and elements of technological equipments which are acceptable for groundwater resources. The vulnerability assessment stems from the application of the register of hazards and a potential threat to individual water source elements within each type of hazard. The proposed procedure is pattern for assessing the risks of disturbance, damage, or destruction of water source by the identified natural or technological hazards and consequently for classification of these risks in relation to emergency water supply. Using of this procedure was verified on selected groundwater resource in particular region, which seems to be as potentially useful for crisis planning system.

Keywords: Hazard, Hydrogeological Structure, Elements, Index, Sensitivity, Water Source, Vulnerability

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1410

Authors: Suk-Hyun Yu, Heeyong Kwon

Abstract:

Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.

Keywords: AI, air quality prediction, neural networks, pattern recognition, PM-10.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 808

Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

Abstract:

Sea level rise threatens to increase the impact of future  storms and hurricanes on coastal communities. Accurate sea level  change prediction and supplement is an important task in determining  constructions and human activities in coastal and oceanic areas. In  this study, support vector machines (SVM) is proposed to predict  daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal  parameter values of kernel function are determined using a genetic  algorithm. The SVM results are compared with the field data and  with back propagation (BP). Among the models, the SVM is superior  to BPNN and has better generalization performance.

 

Keywords: Tides, Prediction, Support Vector Machines, Genetic Algorithm, Back-Propagation Neural Network, Risk, Hazards.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2363

Authors: K. Shimola, M. Krishnaveni

Abstract:

This paper examines vulnerability assessment of water resources in a semi-arid basin using the 4-step approach. The vulnerability assessment framework is developed to study the water resources vulnerability which includes the creation of GIS-based vulnerability maps. These maps represent the spatial variability of the vulnerability index. This paper introduces the 4-step approach to assess vulnerability that incorporates a new set of indicators. The approach is demonstrated using a framework composed of a precipitation data for (1975–2010) period, temperature data for (1965–2010) period, hydrological model outputs and the water resources GIS data base. The vulnerability assessment is a function of three components such as exposure, sensitivity and adaptive capacity. The current water resources vulnerability is assessed using GIS based spatio-temporal information. Rainfall Coefficient of Variation, monsoon onset and end date, rainy days, seasonality indices, temperature are selected for the criterion ‘exposure’. Water yield, ground water recharge, evapotranspiration (ET) are selected for the criterion ‘sensitivity’. Type of irrigation and storage structures are selected for the criterion ‘Adaptive capacity’. These indicators were mapped and integrated in GIS environment using overlay analysis. The five sub-basins, namely Arjunanadhi, Kousiganadhi, Sindapalli-Uppodai and Vallampatti Odai, fall under medium vulnerability profile, which indicates that the basin is under moderate stress of water resources. The paper also explores prioritization of sub-basinwise adaptation strategies to climate change based on the vulnerability indices.

Keywords: Adaptive capacity, exposure, overlay analysis, sensitivity, vulnerability.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1100

Authors: Myungsook Klassen

Abstract:

Training neural networks to capture an intrinsic property of a large volume of high dimensional data is a difficult task, as the training process is computationally expensive. Input attributes should be carefully selected to keep the dimensionality of input vectors relatively small. Technical indexes commonly used for stock market prediction using neural networks are investigated to determine its effectiveness as inputs. The feed forward neural network of Levenberg-Marquardt algorithm is applied to perform one step ahead forecasting of NASDAQ and Dow stock prices.

Keywords: Stock Market Prediction, Neural Networks, Levenberg-Marquadt Algorithm, Technical Indexes

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1923

Authors: Marina Velikova, Hennie Daniels, Ad Feelders

Abstract:

In many applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. Here we consider partially monotone problems, where the target variable depends monotonically on some of the predictor variables but not all. We propose an approach to build partially monotone models based on the convolution of monotone neural networks and kernel functions. The results from simulations and a real case study on house pricing show that our approach has significantly better performance than partially monotone linear models. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.

Keywords: Mixture models, monotone neural networks, partially monotone models, partially monotone problems.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1595

Authors: Sulaiman A. Alateyah, Richard M Crowder, Gary B Wills

Abstract:

This paper discusses E-government, in particular the challenges that face adoption in Saudi Arabia. E-government can be defined based on an existing set of requirements. In this research we define E-government as a matrix of stakeholders: governments to governments, governments to business and governments to citizens, using information and communications technology to deliver and consume services. E-government has been implemented for a considerable time in developed countries. However, E-government services still face many challenges in their implementation and general adoption in many countries including Saudi Arabia. It has been noted that the introduction of E-government is a major challenge facing the government of Saudi Arabia, due to possible concerns raised by its citizens. In addition, the literature review and the discussion identify the influential factors that affect the citizens’ intention to adopt E-government services in Saudi Arabia. Consequently, these factors have been defined and categorized followed by an exploratory study to examine the importance of these factors. Therefore, this research has identified factors that determine if the citizen will adopt E-government services and thereby aiding governments in accessing what is required to increase adoption.

Keywords: E-government, adoption, factors, G2C, intention, citizens’ intention, influential factors.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2229

Authors: Achela K. Fernando, Xiujuan Zhang, Peter F. Kinley

Abstract:

A feed-forward, back-propagation Artificial Neural Network (ANN) model has been used to forecast the occurrences of wastewater overflows in a combined sewerage reticulation system. This approach was tested to evaluate its applicability as a method alternative to the common practice of developing a complete conceptual, mathematical hydrological-hydraulic model for the sewerage system to enable such forecasts. The ANN approach obviates the need for a-priori understanding and representation of the underlying hydrological hydraulic phenomena in mathematical terms but enables learning the characteristics of a sewer overflow from the historical data. The performance of the standard feed-forward, back-propagation of error algorithm was enhanced by a modified data normalizing technique that enabled the ANN model to extrapolate into the territory that was unseen by the training data. The algorithm and the data normalizing method are presented along with the ANN model output results that indicate a good accuracy in the forecasted sewer overflow rates. However, it was revealed that the accurate forecasting of the overflow rates are heavily dependent on the availability of a real-time flow monitoring at the overflow structure to provide antecedent flow rate data. The ability of the ANN to forecast the overflow rates without the antecedent flow rates (as is the case with traditional conceptual reticulation models) was found to be quite poor.

Keywords: Artificial Neural Networks, Back-propagationlearning, Combined sewer overflows, Forecasting.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1491

Authors: K. A. Laptinskiy, S. A. Burikov, A. M. Vervald, S. A. Dolenko, T. A. Dolenko

Abstract:

The article presents the results of the application of artificial neural networks to separate the fluorescent contribution of nanodiamonds used as biomarkers, adsorbents and carriers of drugs in biomedicine, from a fluorescent background of own biological fluorophores. The principal possibility of solving this problem is shown. Use of neural network architecture let to detect fluorescence of nanodiamonds against the background autofluorescence of egg white with high accuracy - better than 3 ug/ml.

Keywords: Artificial neural networks, fluorescence, data aggregation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2079

Authors: Seyed Hossein Iranmanesh, Mansoureh Zarezadeh

Abstract:

This paper presents an application of Artificial Neural Network (ANN) to forecast actual cost of a project based on the earned value management system (EVMS). For this purpose, some projects randomly selected based on the standard data set , and it is produced necessary progress data such as actual cost ,actual percent complete , baseline cost and percent complete for five periods of project. Then an ANN with five inputs and five outputs and one hidden layer is trained to produce forecasted actual costs. The comparison between real and forecasted data show better performance based on the Mean Absolute Percentage Error (MAPE) criterion. This approach could be applicable to better forecasting the project cost and result in decreasing the risk of project cost overrun, and therefore it is beneficial for planning preventive actions.

Keywords: Earned Value Management System (EVMS), Artificial Neural Network (ANN), Estimate At Completion, Forecasting Methods, Project Performance Measurement.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2727

Authors: Iqbal Hossain, Dr. Monzur Imteaz, Dr. Shirley Gato-Trinidad, Prof. Abdallah Shanableh

Abstract:

Estimation of runoff water quality parameters is required to determine appropriate water quality management options. Various models are used to estimate runoff water quality parameters. However, most models provide event-based estimates of water quality parameters for specific sites. The work presented in this paper describes the development of a model that continuously simulates the accumulation and wash-off of water quality pollutants in a catchment. The model allows estimation of pollutants build-up during dry periods and pollutants wash-off during storm events. The model was developed by integrating two individual models; rainfall-runoff model, and catchment water quality model. The rainfall-runoff model is based on the time-area runoff estimation method. The model allows users to estimate the time of concentration using a range of established methods. The model also allows estimation of the continuing runoff losses using any of the available estimation methods (i.e., constant, linearly varying or exponentially varying). Pollutants build-up in a catchment was represented by one of three pre-defined functions; power, exponential, or saturation. Similarly, pollutants wash-off was represented by one of three different functions; power, rating-curve, or exponential. The developed runoff water quality model was set-up to simulate the build-up and wash-off of total suspended solids (TSS), total phosphorus (TP) and total nitrogen (TN). The application of the model was demonstrated using available runoff and TSS field data from road and roof surfaces in the Gold Coast, Australia. The model provided excellent representation of the field data demonstrating the simplicity yet effectiveness of the proposed model.

Keywords: Catchment, continuous pollutants build-up, pollutants wash-off, runoff, runoff water quality model.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3096

Authors: Dariush Jafari, S. Mostafa Nowee

Abstract:

In this study a ternary system containing sodium chloride as solute, water as primary solvent and ethanol as the antisolvent was considered to investigate the application of artificial neural network (ANN) in prediction of sodium solubility in the mixture of water as the solvent and ethanol as the antisolvent. The system was previously studied using by Extended UNIQUAC model by the authors of this study. The comparison between the results of the two models shows an excellent agreement between them (R2=0.99), and also approves the capability of ANN to predict the thermodynamic behavior of ternary electrolyte systems which are difficult to model.

Keywords: Thermodynamic modeling, ANN, solubility, ternary electrolyte system.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2131

Authors: Ali Reza Rahmani, Mohamad Taghi Samadi, Maryam Khodadadi

Abstract:

A laboratory set-up was designed to survey the effectiveness of UV/O3 advanced oxidation process (AOP) for the removal of Carbaryl from polluted water in batch reactor. The study was carried out by UV/O3 process for water samples containing 1 to 20 mg/L of Carbaryl in distilled water. Also the range of drinking water resources adjusted in synthetic water and effects of contact time, pH and Carbaryl concentration were studied. The residual pesticide concentration was determined by applying high performance liquid chromatography (HPLC). The results indicated that increasing of retention time and pH, enhances pesticide removal efficiency. The removal efficiency has been affected by pesticide initial concentration. Samples with low pesticide concentration showed a remarkable removal efficiency compared to the samples with high pesticide concentration. AOP method showed the removal efficiencies of 80% to 100%. Although process showed high performance for removal of pesticide from water samples, this process has different disadvantages including complication, intolerability, difficulty of maintenance and equipmental and structural requirements.

Keywords: AOP, Carbaryl, Pesticides, Water treatment.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2345

Authors: Hidehiko Okada

Abstract:

Evolution strategy (ES) is a well-known instance of evolutionary algorithms, and there have been many studies on ES. In this paper, the author proposes an extended ES for solving fuzzy-valued optimization problems. In the proposed ES, genotype values are not real numbers but fuzzy numbers. Evolutionary processes in the ES are extended so that it can handle genotype instances with fuzzy numbers. In this study, the proposed method is experimentally applied to the evolution of neural networks with fuzzy weights and biases. Results reveal that fuzzy neural networks evolved using the proposed ES with fuzzy genotype values can model hidden target fuzzy functions even though no training data are explicitly provided. Next, the proposed method is evaluated in terms of variations in specifying fuzzy numbers as genotype values. One of the mostly adopted fuzzy numbers is a symmetric triangular one that can be specified by its lower and upper bounds (LU) or its center and width (CW). Experimental results revealed that the LU model contributed better to the fuzzy ES than the CW model, which indicates that the LU model should be adopted in future applications of the proposed method.

Keywords: Evolutionary algorithm, evolution strategy, fuzzy number, feedforward neural network, neuroevolution.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1526

Authors: Wen-Tsann Lin, Yung-Tsan Jou, Yih-Chuan Wu, Yuan-Du Hsiao

Abstract:

The Emergency Department of a medical center in Taiwan cooperated to conduct the research. A predictive model of triage system is contracted from the contract procedure, selection of parameters to sample screening. 2,000 pieces of data needed for the patients is chosen randomly by the computer. After three categorizations of data mining (Multi-group Discriminant Analysis, Multinomial Logistic Regression, Back-propagation Neural Networks), it is found that Back-propagation Neural Networks can best distinguish the patients- extent of emergency, and the accuracy rate can reach to as high as 95.1%. The Back-propagation Neural Networks that has the highest accuracy rate is simulated into the triage acuity expert system in this research. Data mining applied to the predictive model of the triage acuity expert system can be updated regularly for both the improvement of the system and for education training, and will not be affected by subjective factors.

Keywords: Back-propagation Neural Networks, Data Mining, Emergency Department, Triage System.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2282

Authors: Md Asif Imrul, Maria Rafique, M. Habibur Rahman

Abstract:

The study deals with an investigation into the impact of the relocation of tannery industry on water quality parameters of Buriganga. For this purpose, previous records have been collected from authentic data resources and for the attainment of present values, several samples were collected from three major locations of the Buriganga River during summer and winter seasons in 2018 to determine the distribution and variation of water quality parameters. Samples were collected six ft below the river water surface. Analysis indicates slightly acidic to slightly alkaline (6.8-7.49) in nature. Bio-Chemical Oxygen Demand, Total Dissolved Solids, Total Solids (TS) & Total Suspended Solids (TSS) have been found greater in summer. On the other hand, Dissolved Oxygen is found greater in rainy seasons. Relocation shows improvement in water quality parameters. Though the improvement related to relocation of tannery industry is not adequate to turn the water body to be an inhabitable place for aquatic lives.

Keywords: Buriganga river, river pollution, tannery industry, water quality parameters.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 875

Authors: Shin Je Lee, Go Bong Choi, Jeong Cheol Seo, Jong Min Lee, Gibaek Lee

Abstract:

Water pipe network is installed underground and once equipped, it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate or pressure. The transient model describing water flow in pipelines is presented and simulated using MATLAB. The fault situations such as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the better fault detection performance.

Keywords: fault detection, water pipeline model, fast Fourier transform, discrete wavelet transform.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2320

Authors: Naiqin Feng, Fang Wang, Yuhui Qiu

Abstract:

A new approach to promote the generalization ability of neural networks is presented. It is based on the point of view of fuzzy theory. This approach is implemented through shrinking or magnifying the input vector, thereby reducing the difference between training set and testing set. It is called “shrinking-magnifying approach" (SMA). At the same time, a new algorithm; α-algorithm is presented to find out the appropriate shrinking-magnifying-factor (SMF) α and obtain better generalization ability of neural networks. Quite a few simulation experiments serve to study the effect of SMA and α-algorithm. The experiment results are discussed in detail, and the function principle of SMA is analyzed in theory. The results of experiments and analyses show that the new approach is not only simpler and easier, but also is very effective to many neural networks and many classification problems. In our experiments, the proportions promoting the generalization ability of neural networks have even reached 90%.

Keywords: Fuzzy theory, generalization, misclassification rate, neural network.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1515

Authors: Thomas Behan, Zaiyi Liao, Lian Zhao, Chunting Yang

Abstract:

In this paper, low end Digital Signal Processors (DSPs) are applied to accelerate integer neural networks. The use of DSPs to accelerate neural networks has been a topic of study for some time, and has demonstrated significant performance improvements. Recently, work has been done on integer only neural networks, which greatly reduces hardware requirements, and thus allows for cheaper hardware implementation. DSPs with Arithmetic Logic Units (ALUs) that support floating or fixed point arithmetic are generally more expensive than their integer only counterparts due to increased circuit complexity. However if the need for floating or fixed point math operation can be removed, then simpler, lower cost DSPs can be used. To achieve this, an integer only neural network is created in this paper, which is then accelerated by using DSP instructions to improve performance.

Keywords: Digital Signal Processor (DSP), Integer Neural Network(INN), Low Cost Neural Network, Integer Neural Network DSPImplementation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1768

Authors: Qasim M. Kriri

Abstract:

Few Saudi Arabia production companies face financial profit issues until this moment. This work presents a linear integer programming model that solves a production problem of a Saudi Food Company in Saudi Arabia. An optimal solution to the above-mentioned problem is a Linear Programming solution. In this regard, the main purpose of this project is to maximize profit. Linear Programming Technique has been used to derive the maximum profit from production of natural juice at Saudi Food Co. The operations of production of the company were formulated and optimal results are found out by using Lindo Software that employed Sensitivity Analysis and Parametric linear programming in order develop Linear Programming. In addition, the parameter values are increased, then the values of the objective function will be increased.

Keywords: Parameter linear programming, objective function, sensitivity analysis, optimize profit.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2861

Authors: Azadeh Vaziri

Abstract:

Using effluent as a sustainable water resource for agriculture not only could provide part of water needs but also would save the existing water resources, durably. Vinasse, the effluent of ethyl alcohol factories, a by-product, which is derived from sugarcane molasses, is one of the water resources that could be effectively utilized for agricultural purposes. In the present study in order to investigate the application of different ratios of water: vinasse on germination and growth of barley seedlings an experiment was designed in pots with completely randomized design with three replications and control treatment. The consequences of four irrigation levels were studied with different water: effluent ratios (100% water, 90% water & 10% effluent, 75% water & 25% effluent, 50% water & 50% effluent) on germination and growth of barley seedling components in sandy-loam soil. The results showed that, with increasing the percentage of vinasse in the irrigation admixture, the germination percentage in barley seedlings decreased, significantly, so that the decrease in germination in comparison with the control samples in the second and third treatments was 20% and 93.33%, respectively. Seed germination percentage was about 46.66. The average stem length in seedlings was 14.3 mm and the average root length was 9.37 mm. The averages of the soils Electrical Conductivity (EC) and pH which were under irrigation with different ratios of vinasse (dSm-1) were 5.85 and 7.32, respectively, which showed a 76.2% increase in soil salinity.

Keywords: Electrical Conductivity, effluent, germination, vinasse, barley.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 364

Authors: D. Triantakonstantis, D. Stathakis

Abstract:

Urban areas have been expanded throughout the globe. Monitoring and modelling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modelling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: Artificial Neural Networks, CORINE, Urban Atlas, Urban Growth Prediction.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3417

Authors: Evgeniy Burlutskiy

Abstract:

The paper presents a one-dimensional transient mathematical model of thermal oil-water two-phase emulsion flows in pipes. The set of the mass, momentum and enthalpy conservation equations for the continuous fluid and droplet phases are solved. Two friction correlations for the continuous fluid phase to wall friction are accounted for in the model and tested. The aerodynamic drag force between the continuous fluid phase and droplets is modeled, too. The density and viscosity of both phases are assumed to be constant due to adiabatic experimental conditions. The proposed mathematical model is validated on the experimental measurements of oil-water emulsion flows in horizontal pipe [1,2]. Numerical analysis on single- and two-phase oil-water flows in a pipe is presented in the paper. The continuous oil flow having water droplets is simulated. Predictions, which are performed by using the presented model, show excellent agreement with the experimental data if the water fraction is equal or less than 10%. Disagreement between simulations and measurements is increased if the water fraction is larger than 10%.

Keywords: Mathematical model, Oil-Water, Pipe flows.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2267

Authors: Anupama Pande, Vishik Goel

Abstract:

A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.

Keywords: Complex valued neural network, Radial BasisFunction, Image recognition.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2382

Authors: J. B. Qin, X. H. Jiang, Y. T. Ge

Abstract:

The growing awareness of global warming potential has internationally aroused interest and demand in reducing greenhouse gas emissions produced by human activity. Much national energy in the UK had been consumed in the residential sector mainly for space heating and domestic hot water production. Currently, gas boilers are mostly applied in the domestic water heating which contribute significantly to excessive CO₂ emissions and consumption of primary energy resources. The issues can be solved by popularizing heat pump systems that are attributable to higher performance efficiency than those of traditional gas boilers. Even so, the heat pump system performance can be further enhanced if the dissolved gases in its hot water circuit can be efficiently discharged.  To achieve this target, the bubble behaviors in the heat pump water heating system need to be extensively investigated. In this paper, by varying different experimental conditions, the effects of various heat pump hot water side parameters on gas microbubble diameters were measured and analyzed. Correspondingly, the effect of each parameter has been investigated. These include varied system pressures, water flow rates, saturation ratios and heat outputs. The results measurement showed that the water flow rate is the most significant parameter to influence on gas microbubble productions. The research outcomes can significantly contribute to the understanding of gas bubble behaviors at domestic heat pump water heating systems and thus the efficient way for the discharging of the associated dissolved gases.  

Keywords: Dissolved gases in water, heat pump, domestic water heating system, microbubble formation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 816

Authors: H. Alaleh, N. Hedayat, A. Alaleh, H. Ayazi, A. Ruhani

Abstract:

Run-offs are considered as important hydrological factors in feasibility studies of river engineering and irrigation-related projects under arid and semi-arid condition. Flood control is one of the crucial factor, the management of which while mitigates its destructive consequences, abstracts considerable volume of renewable water resources. The methodology applied here was based on Mizumura, which applied a mathematical model for simple tank to simulate the rainfall-run-off process in a particular water basin using the data from the observational hydrograph. The model was applied in the Dez River water basin adjacent to Greater Dezful region, Iran in order to simulate and estimate the floods. Results indicated that the calculated hydrographs using the simple tank method, SCS-CN model and the observation hydrographs had a close proximity. It was also found that on average the flood time and discharge peaks in the simple tank were closer to the observational data than the CN method. On the other hand, the calculated flood volume in the CN model was significantly closer to the observational data than the simple tank model.

Keywords: Simple tank, Dez River, run-off, lag time, excess rainfall.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2569

Authors: Hayder H. Kareem, Shunqi Pan

Abstract:

Groundwater is a vital water resource in many areas in the world, particularly in the Middle-East region where the water resources become scarce and depleting. Sustainable management and planning of the groundwater resources become essential and urgent given the impact of the global climate change. In the recent years, numerical models have been widely used to predict the flow pattern and assess the water resources security, as well as the groundwater quality affected by the contaminants transported. In this study, MODFLOW is used to study the current status of groundwater resources and the risk of water resource security in the region centred at Al-Najaf City, which is located in the mid-west of Iraq and adjacent to the Euphrates River. In this study, a conceptual model is built using the geologic and hydrogeologic collected for the region, together with the Digital Elevation Model (DEM) data obtained from the "Global Land Cover Facility" (GLCF) and "United State Geological Survey" (USGS) for the study area. The computer model is also implemented with the distributions of 69 wells in the area with the steady pro-defined hydraulic head along its boundaries. The model is then applied with the recharge rate (from precipitation) of 7.55 mm/year, given from the analysis of the field data in the study area for the period of 1980-2014. The hydraulic conductivity from the measurements at the locations of wells is interpolated for model use. The model is calibrated with the measured hydraulic heads at the locations of 50 of 69 wells in the domain and results show a good agreement. The standard-error-of-estimate (SEE), root-mean-square errors (RMSE), Normalized RMSE and correlation coefficient are 0.297 m, 2.087 m, 6.899% and 0.971 respectively. Sensitivity analysis is also carried out, and it is found that the model is sensitive to recharge, particularly when the rate is greater than (15mm/year). Hydraulic conductivity is found to be another parameter which can affect the results significantly, therefore it requires high quality field data. The results show that there is a general flow pattern from the west to east of the study area, which agrees well with the observations and the gradient of the ground surface. It is found that with the current operational pumping rates of the wells in the area, a dry area is resulted in Al-Najaf City due to the large quantity of groundwater withdrawn. The computed water balance with the current operational pumping quantity shows that the Euphrates River supplies water into the groundwater of approximately 11759 m³/day, instead of gaining water of 11178 m³/day from the groundwater if no pumping from the wells. It is expected that the results obtained from the study can provide important information for the sustainable and effective planning and management of the regional groundwater resources for Al-Najaf City.

Keywords: Al-Najaf city, conceptual modelling, groundwater, unconfined aquifer, visual MODFLOW.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1886

Authors: Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed

Abstract:

In this paper, we propose a supervised method for color image classification based on a multilevel sigmoidal neural network (MSNN) model. In this method, images are classified into five categories, i.e., “Car", “Building", “Mountain", “Farm" and “Coast". This classification is performed without any segmentation processes. To verify the learning capabilities of the proposed method, we compare our MSNN model with the traditional Sigmoidal Neural Network (SNN) model. Results of comparison have shown that the MSNN model performs better than the traditional SNN model in the context of training run time and classification rate. Both color moments and multi-level wavelets decomposition technique are used to extract features from images. The proposed method has been tested on a variety of real and synthetic images.

Keywords: Image classification, multi-level neural networks, feature extraction, wavelets decomposition.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1623