Driving licensing renewal policy using neural network-based probabilistic decision support system

This paper investigates neural network-based probabilistic decision support system to assess drivers' knowledge for the objective of developing a renewal policy of driving licences. The probabilistic model correlates drivers' demographic data to their results in a simulated written driving exam (SWDE). The probabilistic decision support system classifies drivers' into two groups of passing and failing a SWDE. Knowledge assessment of drivers within a probabilistic framework allows quantifying and incorporating uncertainty information into the decision-making system. The results obtained in a Jordanian case study indicate that the performance of the probabilistic decision support systems is more reliable than conventional deterministic decision support systems. Implications of the proposed probabilistic decision support systems on the renewing of the driving licences decision and the possibility of including extra assessment methods are discussed

Probabilistic multiple model neural network based leak detection system:experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results

Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks

Pressurized irrigation systems are widespread among other alternatives in Mediterranean countries. Since the initial investment costs of pressurized irrigation systems are quite high, it is crucial to determine design parameters such as pipe diameter. Most of the current optimization techniques for pipe diameter selection are based on linear, non-linear, and dynamic programming models. The ultimate aim of these techniques is to produce solutions to problems with less cost and computation time. In this study, a novel approach for determining pipe diameter was proposedusing Artificial Neural Networks (ANN) as an alternative to existing models. For this purpose, three pressurized irrigation systems were investigated. Different ANN architectures were created and tested using hydrant level parameters of the irrigation systems, such as irrigated area per hydrant, hydrant discharge, pipe length, and hydrant elevation. Different training algorithms, transfer functions, and hidden neuron numbers were tried to determine the best ANN model for each irrigation system. Using multilayer feed-forward ANN architecture, the highest coefficients of determination were found to be 0.97, 0.93, and 0.83 for irrigation systems investigated. It was concluded that pipe diameters could be determined by using artificial neural networks in the planning of pressurized irrigation systems

Exploring the optimal potential of transient reflection method through mel-frequency ceptrums coefficient and artificial neural network for leak detection and size estimation in water distribution systems

Water pipeline systems are critical infrastructures that provide potable water to communities. The design and operation of these systems are complex and require careful consideration of various factors, such as system reliability. Regular maintenance and inspection of pipelines and other components are necessary to prevent leaks and ensure that the system operates effectively. The efficient detection and accurate estimation of leaks in water distribution systems are crucial for maintaining the integrity and functionality of the infrastructure. This research aims to unleash the full potential of the transient reflection method through the integration of Mel-Frequency Cepstral Coefficients (MFCC) and Artificial Neural Network (ANN) techniques for leak detection and size estimation in water distribution systems. By leveraging the combined power of signal processing and machine learning, this study aim to advance the state-of-the-art methodologies for leak detection and size estimation, providing more accurate and efficient approaches based on transient reflection method. The objectives of this research are to explores the application of MFCC as a signal processing technique to extract vital information from the transient reflection signals. The transient reflection signals carry valuable insights into the characteristics of the water distribution system and can aid in identifying leaks. Furthermore to investigate and select significant features derived from the transient reflection signals that reflect the nature of leak size. Finally, is to develop and validate an ANN-based model for leak size estimation that harnesses the power of the extracted TRM features. To achieve these objectives, extensive experimentation and analysis will be conducted using transient reflection method obtained from laboratory scale water distribution systems. The data will be collected from various sizes of leaks. The collected dataset will serve as the foundation for training and validating the developed ANN model. Performance evaluation metrics, such as accuracy, precision, recall, and mean squared error, will be utilized to assess the effectiveness and reliability of the leak detection and size estimation technique. The expected outcomes of this research include advancements in leak detection and size estimation techniques in water distribution systems. The integration of MFCC and ANN techniques has the potential to significantly improve the accuracy and efficiency of leak detection, leading to timely identification and mitigation of leaks. The developed estimation model can aid in assessing the severity of leaks, enabling more effective allocation of resources for repair and maintenance activities. Ultimately, the findings of this research will contribute to the enhancement of water distribution system management, promoting water conservation and minimizing the adverse impacts of leaks on infrastructure and the environment. In conclusion, this research endeavors to unleash the full potential of the transient reflection method through the integration of MFCC and ANN techniques for leak detection and size estimation in water distribution systems. By leveraging signal processing and machine learning, this study aims to advance the state-of-the-art methodologies and provide more accurate and efficient approaches to address the challenges associated with leak detection and size estimation. The outcomes of this research have the potential to significantly benefit water management authorities, utilities, and researchers working in the field of water distribution system management and conservation

An Integrated Decision Support System for the Planning, Analysis, Management and Rehabilitation of Pressurised Irrigation Distribution Systems

Water scarcity is a mounting problem in arid and semi-arid regions such as the Mediterranean. Therefore, smarter and more effective water management is required, especially in irrigated agriculture. Irrigation infrastructure such as pressurized irrigation distribution systems (PIDSs) play an important role for the intensification of agricultural production in the Mediterranean region. However, the operation and management of these systems can be complex as they involve several intertwined processes, which need to be considered simultaneously. For this reason, numerous decision support systems (DSSs) have been developed and are available to deal with these processes, but as independent components. To this end, a comprehensive DSS called DESIDS has been developed and tested in the framework of this research. This DSS has been developed bearing in mind the need of irrigation district managers for an integrated tool that can assist them in taking strategic decisions for managing and developing reliable, adequate and sustainable water distribution plans, which provide the best services to farmers. Hence, four modules were integrated in DESIDS: i) the irrigation demand and scheduling module; ii) the hydraulic analysis module; iii) the operation and management modules; and iv) the design and rehabilitation module. DESIDS was tested on different case studies located in the Apulia region, where it proved to be a valuable tool for irrigation district managers as it provides a wide range of decision options for proper operation and management of PIDSs. All this is obtained through a DSS that offers: i) high level of interactivity; ii) complete control of the irrigation managers; iii) adaptability and flexibility to the problems related to the operation of PIDSs; and iv) effectiveness in assisting irrigation managers with the decision making. The developed DSS can be used as a platform for future integrations and expansions to include other processes needed for better decision-making support

Estudos de Aprimoramento de Algoritmo de Calibração e Aplicação em Rede de Distribuição de Água de Cambuí (MG).

Diante da indiscutível necessidade de um gerenciamento de qualidade dos recursos hídricos, principiando pelas redes de distribuição de água para o abastecimento humano, os modelos matemáticos da previsão do comportamento hidráulico das mesmas são essenciais, bem como a revisão dos seus parâmetros e o seu aprimoramento, frente às constantes mudanças físicas sucedidas ao longo do tempo em um sistema. A calibração das redes de distribuição de água é uma maneira de efetuar tais procedimentos nos modelos hidráulicos, mas diversas são as dificuldades encontradas na calibração de uma rede real, dentre elas a precariedade das informações nos cadastros das companhias de água. Diferentes métodos de calibração foram e são propostos na literatura, geralmente com equações analíticas e variadas técnicas de otimização. Este trabalho propõe o aprimoramento de módulos do método de calibração de redes de distribuição de água proposto por Silva (2003), onde empregando a ferramenta de busca dos algoritmos genéticos, o autor calibra duas redes de distribuição de água reais da cidade de São Carlos (SP), ajustando parâmetros principalmente de rugosidades e de vazamentos. O aprimoramento do modelo constitui a introdução de uma nova variável de decisão, a demanda nodal, que primeiramente atribui valores aleatórios de demanda aos nós e após trabalha segundo o modelo de demanda dirigida por pressão de Tucciarelli, Criminisi e Termini (1999). Os testes dos modelos implementados são testados para uma rede hipotética de distribuição de água e duas redes reais. A primeira rede real é a mesma ensaiada por Silva (2003), o setor “Monte Carlo” e a segunda rede real, sobre a qual foram realizados extensivos trabalhos de campo, é um setor da cidade de Cambuí (MG). O efeito da introdução do modelo da demanda dirigida por pressão nos resultados de calibração se mostrou mais significante para a rede hipotética e para a rede real do setor “Monte Carlo”, obtendo valores simulados de pressão com erro absoluto médio menor que 0,5 mca para 100% dos nós da rede hipotética e menor que 0,75 mca para 95% da rede real do setor “Monte Carlo”. No setor real de Cambuí (MG) demonstra-se a necessidade de novos estudos para que os modelos de calibração se ajustem, como por exemplo os coeficientes de vazamento. Tais estudos naturalmente são necessários para calibração em redes de escala real

Development of a success model for Water Management Information Systems

The management of water resources traverses many disciplines and involves multiple stakeholders. Water Management Information Systems (WMIS) is a combination of technological resources - software and hardware - and tools implemented to enhance the roles and functions, and the decision-making processes of water resource management. WMIS have been acknowledged to be a critical actor and part of the water resources management processes. Though the water resources management literature presents substantial evidence to back this claim, there is insufficient evidence of research in the IS literature to understand factors that affect the success of WMIS implementations. More importantly, due to the complexity of managing the resource, factors surrounding the systems and organisational context of water management institutions affect its implementations. The aim of this study is thus to develop, test and validate a model for understanding WMIS success in the water resources management context. This integrated model combines the system and organisational factors to develop the success model. The WMIS success model was conceptualized and operationalised based on the principles of water resources management, specifically the Integrated Water Resources Management (IWRM), and two IS models - HOT-Fit Framework and DeLone and McLean IS success model. The model consisted of the system and organisational factors, and a set of outcome constructs or net benefits - WMIS for Water Management Operations and WMIS for Water Management Decision-Making - that represented WMIS success. The system factors consisted of five dimensions namely; WMIS System Quality, WMIS Information Quality, Service Quality, System Use and User Satisfaction; whereas the organisation factors consisted of Leadership, Structure and Environment constructs. The model was tested and validated using cross-sectional data collected from users of WMIS from various designations of the Department of Water and Sanitation in the City of Cape Town metropolitan municipality in Cape Town, South Africa. The study recorded a 38% response rate. To analyse and validate the model, a Partial Least Squares (PLS) approach to Structural Equation Modelling (SEM) was employed. Overall, the variance explained in WMIS for Water Management Operations was 53% whiles WMIS for Water Management Decision-Making was 12%. The model fit was deemed substantial. The direct, indirect and total effects showed that, for the system factors, User Satisfaction (� =0,69) had the strongest total effect on WMIS for Water Management Operations, whereas System Use (� =0,25) had strongest total effect on WMIS for Water Management Decision-Making; in the organisation dimension, Environment (� =0,12) had the strongest total effect on WMIS for Water Management Operations, whereas Leadership (� =0,19) had the strongest total effect on WMIS for Water Management Decision-Making. User Satisfaction (� =0,69) had the strongest direct and total effect on WMIS for Water Management Operations, whereas System Use (�=0,25) had the strongest direct and total effect on WMIS for Water Management Decision-Making in the human dimension. Though some of the relationships between the constructs were new to the water management context, some of the remaining relationships were consistent with finding from other systems in the IS domain. Further, the findings suggested that Service Quality, which in the contextual sense implied system and IT support staff, must be present onsite within the water management organisations to support WMIS users. Leaders in the various designations must have both and transactional and transformational characteristics. In this regard, they must ensure that they motivate users and commend them when they produce good work that affects the outcomes. Management should also ensure that they pay attention to external environmental factors like accreditation standards that affect their operations. Finally, this research has provided empirical evidence of the development of an integrated WMIS success model that is based on IS models and water resources management principles

Modelling and Simulation of Water Networks based on Least Square Loop Flows State Estimator

<p>Modelling and Simulation of Water Networks based on Least Square Loop Flows State Estimator</p> <p>This Matlab software implements the Least Squares state estimator described in the below papers and which state estimator is based on the loop corrective flows and the variation of nodal demands as independent variables.</p> <ol> <li> <p>Corneliu T.C. Arsene, "Uncertainty Quantification of Water Distribution System Measurement Data based on a Least Squares Loop Flows State Estimator", arXiv:1701.03147, https://arxiv.org/abs/1701.03147, 2017.</p> </li> <li> <p>Corneliu T.C. Arsene, Bogdan Gabrys, “Mixed simulation-state estimation in water distribution systems based on a least squares loop flows state estimator”, Applied Mathematical Modelling, DOI 10.1016/ j.apm.2013.06.012 , 2014.</p> </li> <li> <p>Corneliu T. C. Arsene, Bogdan Gabrys, David Al-Dabass: Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection. Expert Syst. Appl. 39(18): 13214-13224, 2012.</p> </li> <li> <p>Arsene, C.T.C., Bargiela, A., and Al-Dabass, D. “Modelling and Simulation of Network Systems based on Loop Flows Algorithms”, Int. J. of Simulation: Systems,Science & Technology Vol.5, No. 1 & 2, pp61-72, June 2004.</p> </li> <li> <p>Arsene, C.T.C., & Bargiela, A., “Decision support for forecasting and fault diagnosis in water distribution systems – robust loop flows state estimation technique”, In Water Software Systems: theory and applications, Research Studies Press Ltd., UK, Vol. 1, pp. 133-145, 2001.</p> </li> <li> <p>Arsene, C.T.C., Bargiela, A., Al-Dabass, D., “Simulation of Network Systems based on Loop Flows Algorithms”, In the proceedings of the 7th Simulation Society Conference - UKSim 2004, Oxford, U.K., 2004, ISBN 1-84233-099-3, UKSIM-2004.</p> </li> </ol> <p>Please acknowledge the PhD project financed by the Nottingham Trent University of United Kingdom and Mr Corneliu Arsene if you are going to use this software anywhere in your work. This is in addition to the license for this software which is in a different file.</p> <p>It is provided here with no warranty. Direct all questions and requests to [email protected]. Technical details (not to be confused by the name of the files):</p