2,674 research outputs found
Applications of two neuro-based metaheuristic techniques in evaluating ground vibration resulting from tunnel blasting
Peak particle velocity (PPV) caused by blasting is an unfavorable environmental issue that can damage neighboring structures or equipment. Hence, a reliable prediction and minimization of PPV are essential for a blasting site. To estimate PPV caused by tunnel blasting, this paper proposes two neuro-based metaheuristic models: neuro-imperialism and neuro-swarm. The prediction was made based on extensive observation and data collecting from a tunnelling project that was concerned about the presence of a temple near the blasting operations and tunnel site. A detailed modeling procedure was conducted to estimate PPV values using both empirical methods and intelligence techniques. As a fair comparison, a base model considered a benchmark in intelligent modeling, artificial neural network (ANN), was also built to predict the same output. The developed models were evaluated using several calculated statistical indices, such as variance account for (VAF) and a-20 index. The empirical equation findings revealed that there is still room for improvement by implementing other techniques. This paper demonstrated this improvement by proposing the neuro-swarm, neuro-imperialism, and ANN models. The neuro-swarm model outperforms the others in terms of accuracy. VAF values of 90.318% and 90.606% and a-20 index values of 0.374 and 0.355 for training and testing sets, respectively, were obtained for the neuro-swarm model to predict PPV induced by blasting. The proposed neuro-based metaheuristic models in this investigation can be utilized to predict PPV values with an acceptable level of accuracy within the site conditions and input ranges used in this study
Wind generation forecasting methods and proliferation of artificial neural network:A review of five years research trend
To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
In this paper, we present machine learning approaches for characterizing and
forecasting the short-term demand for on-demand ride-hailing services. We
propose the spatio-temporal estimation of the demand that is a function of
variable effects related to traffic, pricing and weather conditions. With
respect to the methodology, a single decision tree, bootstrap-aggregated
(bagged) decision trees, random forest, boosted decision trees, and artificial
neural network for regression have been adapted and systematically compared
using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and
slope. To better assess the quality of the models, they have been tested on a
real case study using the data of DiDi Chuxing, the main on-demand ride hailing
service provider in China. In the current study, 199,584 time-slots describing
the spatio-temporal ride-hailing demand has been extracted with an
aggregated-time interval of 10 mins. All the methods are trained and validated
on the basis of two independent samples from this dataset. The results revealed
that boosted decision trees provide the best prediction accuracy (RMSE=16.41),
while avoiding the risk of over-fitting, followed by artificial neural network
(20.09), random forest (23.50), bagged decision trees (24.29) and single
decision tree (33.55).Comment: Currently under review for journal publicatio
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review
Abstract:Site suitability problems in renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. We further present a framework for the integration of the two concepts in wind and solar energy studies. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies which apply GIS-based method in site suitability were presented with criteria considered. Similarly, country-specific studies in ANFIS-based resource forecasts for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria and the analytical hierarchical process (AHP) has been commonly used for criteria ranking leaving other techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast, and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning
Flood Forecasting Using Machine Learning Methods
This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate
Multi-agent system for flood forecasting in Tropical River Basin
It is well known, the problems related to the generation of floods, their control, and management,
have been treated with traditional hydrologic modeling tools focused on the study and
the analysis of the precipitation-runoff relationship, a physical process which is driven by the
hydrological cycle and the climate regime and that is directly proportional to the generation
of floodwaters. Within the hydrological discipline, they classify these traditional modeling
tools according to three principal groups, being the first group defined as trial-and-error models
(e.g., "black-models"), the second group are the conceptual models, which are categorized
in three main sub-groups as "lumped", "semi-lumped" and "semi-distributed", according to
the special distribution, and finally, models that are based on physical processes, known as
"white-box models" are the so-called "distributed-models". On the other hand, in engineering
applications, there are two types of models used in streamflow forecasting, and which are
classified concerning the type of measurements and variables required as "physically based
models", as well as "data-driven models".
The Physically oriented prototypes present an in-depth account of the dynamics related
to the physical aspects that occur internally among the different systems of a given hydrographic
basin. However, aside from being laborious to implement, they rely thoroughly
on mathematical algorithms, and an understanding of these interactions requires the abstraction
of mathematical concepts and the conceptualization of the physical processes that
are intertwined among these systems. Besides, models determined by data necessitates an
a-priori understanding of the physical laws controlling the process within the system, and
they are bound to mathematical formulations, which require a lot of numeric information
for field adjustments. Therefore, these models are remarkably different from each other
because of their needs for data, and their interpretation of physical phenomena. Although
there is considerable progress in hydrologic modeling for flood forecasting, several significant
setbacks remain unresolved, given the stochastic nature of the hydrological phenomena, is
the challenge to implement user-friendly, re-usable, robust, and reliable forecasting systems,
the amount of uncertainty they must deal with when trying to solve the flood forecasting
problem. However, in the past decades, with the growing environment and development of
the artificial intelligence (AI) field, some researchers have seldomly attempted to deal with
the stochastic nature of hydrologic events with the application of some of these techniques.
Given the setbacks to hydrologic flood forecasting previously described this thesis research
aims to integrate the physics-based hydrologic, hydraulic, and data-driven models under the
paradigm of Multi-agent Systems for flood forecasting by designing and developing a multi-agent system (MAS) framework for flood forecasting events within the scope of tropical
watersheds.
With the emergence of the agent technologies, the "agent-based modeling" and "multiagent
systems" simulation methods have provided applications for some areas of hydro base
management like flood protection, planning, control, management, mitigation, and forecasting
to combat the shocks produced by floods on society; however, all these focused on
evacuation drills, and the latter not aimed at the tropical river basin, whose hydrological
regime is extremely unique.
In this catchment modeling environment approach, it was applied the multi-agent systems
approach as a surrogate of the conventional hydrologic model to build a system that operates
at the catchment level displayed with hydrometric stations, that use the data from hydrometric
sensors networks (e.g., rainfall, river stage, river flow) captured, stored and administered
by an organization of interacting agents whose main aim is to perform flow forecasting and
awareness, and in so doing enhance the policy-making process at the watershed level.
Section one of this document surveys the status of the current research in hydrologic
modeling for the flood forecasting task. It is a journey through the background of related
concerns to the hydrological process, flood ontologies, management, and forecasting. The
section covers, to a certain extent, the techniques, methods, and theoretical aspects and
methods of hydrological modeling and their types, from the conventional models to the
present-day artificial intelligence prototypes, making special emphasis on the multi-agent
systems, as most recent modeling methodology in the hydrological sciences. However, it is
also underlined here that the section does not contribute to an all-inclusive revision, rather
its purpose is to serve as a framework for this sort of work and a path to underline the
significant aspects of the works.
In section two of the document, it is detailed the conceptual framework for the suggested
Multiagent system in support of flood forecasting. To accomplish this task, several works
need to be carried out such as the sketching and implementation of the system’s framework
with the (Belief-Desire-Intention model) architecture for flood forecasting events within the
concept of the tropical river basin. Contributions of this proposed architecture are the
replacement of the conventional hydrologic modeling with the use of multi-agent systems,
which makes it quick for hydrometric time-series data administration and modeling of the
precipitation-runoff process which conveys to flood in a river course. Another advantage is
the user-friendly environment provided by the proposed multi-agent system platform graphical
interface, the real-time generation of graphs, charts, and monitors with the information
on the immediate event taking place in the catchment, which makes it easy for the viewer
with some or no background in data analysis and their interpretation to get a visual idea of
the information at hand regarding the flood awareness.
The required agents developed in this multi-agent system modeling framework for flood
forecasting have been trained, tested, and validated under a series of experimental tasks,
using the hydrometric series information of rainfall, river stage, and streamflow data collected
by the hydrometric sensor agents from the hydrometric sensors.Como se sabe, los problemas relacionados con la generación de inundaciones, su control y
manejo, han sido tratados con herramientas tradicionales de modelado hidrológico enfocados
al estudio y análisis de la relación precipitación-escorrentía, proceso físico que es impulsado
por el ciclo hidrológico y el régimen climático y este esta directamente proporcional a la
generación de crecidas. Dentro de la disciplina hidrológica, clasifican estas herramientas
de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos
empíricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o
semi-distribuidos) dependiendo de la distribución espacial y, por último, los basados en la
física, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido,
clasifican las aplicaciones de predicción de caudal fluvial en la ingeniería de recursos hídricos
en dos tipos con respecto a los valores y parámetros que requieren en: modelos de procesos
basados en la física y la categoría de modelos impulsados por datos.
Los modelos basados en la física proporcionan una descripción detallada de la dinámica
relacionada con los aspectos físicos que ocurren internamente entre los diferentes sistemas de
una cuenca hidrográfica determinada. Sin embargo, aparte de ser complejos de implementar,
se basan completamente en algoritmos matemáticos, y la comprensión de estas interacciones
requiere la abstracción de conceptos matemáticos y la conceptualización de los procesos
físicos que se entrelazan entre estos sistemas. Además, los modelos impulsados por datos no
requieren conocimiento de los procesos físicos que gobiernan, sino que se basan únicamente
en ecuaciones empíricas que necesitan una gran cantidad de datos y requieren calibración
de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos
de datos y de cómo expresan los fenómenos físicos. La elaboración de modelos hidrológicos
para el pronóstico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos
contratiempos importantes, dada la naturaleza estocástica de los fenómenos hidrológicos, es
el desafío de implementar sistemas de pronóstico fáciles de usar, reutilizables, robustos y
confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema
de la predicción de inundaciones. Sin embargo, en las últimas décadas, con el entorno
creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores
rara vez han intentado abordar la naturaleza estocástica de los eventos hidrológicos con la
aplicación de algunas de estas técnicas.
Dados los contratiempos en el pronóstico de inundaciones hidrológicas descritos anteriormente,
esta investigación de tesis tiene como objetivo integrar los modelos hidrológicos,
basados en la física, hidráulicos e impulsados por datos bajo el paradigma de Sistemas de múltiples agentes para el pronóstico de inundaciones por medio del bosquejo y desarrollo
del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicción de
inundaciones en el contexto de cuenca hidrográfica tropical.
Con la aparición de las tecnologías de agentes, se han emprendido algunos enfoques
de simulación recientes en la investigación hidrológica con modelos basados en agentes y
sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificación
de inundaciones, control y gestión de inundaciones y pronóstico de inundaciones, todos estos
enfocado a simulacros de evacuación, y este último no dirigido a la cuenca tropical, cuyo
régimen hidrológico es extremadamente único.
En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas
multi-agente como un sustituto del modelado hidrológico convencional para construir un
sistema que opera a nivel de cuenca con estaciones hidrométricas desplegadas, que utilizan
los datos de redes de sensores hidrométricos (por ejemplo, lluvia , nivel del río, caudal del
río) capturado, almacenado y administrado por una organización de agentes interactuantes
cuyo objetivo principal es realizar pronósticos de caudal y concientización para mejorar las
capacidades de soporte en la formulación de políticas a nivel de cuenca hidrográfica.
La primera sección de este documento analiza el estado del arte sobre la investigación actual
en modelos hidrológicos para la tarea de pronóstico de inundaciones. Es un viaje a través
de los antecedentes preocupantes relacionadas con el proceso hidrológico, las ontologías de
inundaciones, la gestión y la predicción. El apartado abarca, en cierta medida, las técnicas,
métodos y aspectos teóricos y métodos del modelado hidrológico y sus tipologías, desde
los modelos convencionales hasta los prototipos de inteligencia artificial actuales, haciendo
hincapié en los sistemas multi-agente, como un enfoque de simulación reciente en la investigación
hidrológica. Sin embargo, se destaca que esta sección no contribuye a una revisión
integral, sino que su propósito es servir de marco para este tipo de trabajos y una guía para
subrayar los aspectos significativos de los trabajos.
En la sección dos del documento, se detalla el marco de trabajo propuesto para el sistema
multi-agente para el pronóstico de inundaciones. Los trabajos realizados comprendieron el
diseño y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo
Creencia-Deseo-Intención) para la predicción de eventos de crecidas dentro del concepto
de cuenca hidrográfica tropical. Las contribuciones de esta arquitectura propuesta son el
reemplazo del modelado hidrológico convencional con el uso de sistemas multi-agente, lo
que agiliza la administración de las series de tiempo de datos hidrométricos y el modelado
del proceso de precipitación-escorrentía que conduce a la inundación en el curso de un río.
Otra ventaja es el entorno amigable proporcionado por la interfaz gráfica de la plataforma del
sistema multi-agente propuesto, la generación en tiempo real de gráficos, cuadros y monitores
con la información sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace
fácil para el espectador con algo o sin experiencia en análisis de datos y su interpretación
para tener una idea visual de la información disponible con respecto a la cognición de las
inundaciones.
Los agentes necesarios desarrollados en este marco de modelado de sistemas multi-agente
para el pronóstico de inundaciones han sido entrenados, probados y validados en una serie de tareas experimentales, utilizando la información de la serie hidrométrica de datos de lluvia,
nivel del río y flujo del curso de agua recolectados por los agentes sensores hidrométricos de
los sensores hidrométricos de campo.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: María Araceli Sanchis de Miguel.- Secretario: Juan Gómez Romero.- Vocal: Juan Carlos Corrale
Hybrid artificial intelligence algorithms for short-term load and price forecasting in competitive electric markets
The liberalization and deregulation of electric markets forced the various participants to accommodate several challenges, including: a considerable accumulation of new generation capacity from renewable sources (fundamentally wind energy), the unpredictability associated with these new forms of generation and new consumption patterns, contributing to further electricity prices volatility (e.g. the Iberian market).
Given the competitive framework in which market participants operate, the existence of efficient computational forecasting techniques is a distinctive factor. Based on these forecasts a suitable bidding strategy and an effective generation systems operation planning is achieved, together with an improved installed transmission capacity exploitation, results in maximized profits, all this contributing to a better energy resources utilization.
This dissertation presents a new hybrid method for load and electricity prices forecasting, for one day ahead time horizon. The optimization scheme presented in this method, combines the efforts from different techniques, notably artificial neural networks, several optimization algorithms and wavelet transform. The method’s validation was made using different real case studies. The subsequent comparison (accuracy wise) with published results, in reference journals, validated the proposed hybrid method suitability.O processo de liberalização e desregulação dos mercados de energia elétrica, obrigou os diversos participantes a acomodar uma série de desafios, entre os quais: a acumulação considerável de nova capacidade de geração proveniente de origem renovável (fundamentalmente energia eólica), a imprevisibilidade associada a estas novas formas de geração e novos padrões de consumo. Resultando num aumento da volatilidade associada aos preços de energia elétrica (como é exemplo o mercado ibérico).
Dado o quadro competitivo em que os agentes de mercado operam, a existência de técnicas computacionais de previsão eficientes, constituí um fator diferenciador. É com base nestas previsões que se definem estratégias de licitação e se efetua um planeamento da operação eficaz dos sistemas de geração que, em conjunto com um melhor aproveitamento da capacidade de transmissão instalada, permite maximizar os lucros, realizando ao mesmo tempo um melhor aproveitamento dos recursos energéticos.
Esta dissertação apresenta um novo método híbrido para a previsão da carga e dos preços da energia elétrica, para um horizonte temporal a 24 horas. O método baseia-se num esquema de otimização que reúne os esforços de diferentes técnicas, nomeadamente redes neuronais artificiais, diversos algoritmos de otimização e da transformada de wavelet. A validação do método foi feita em diferentes casos de estudo reais. A posterior comparação com resultados já publicados em revistas de referência, revelou um excelente desempenho do método hibrido proposto
Big Data Analysis application in the renewable energy market: wind power
Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías
mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar
mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición
de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais
e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos
diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía
de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións.
Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para
proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)
Intelligent feature selection for neural regression : techniques and applications
Feature Selection (FS) and regression are two important technique categories in
Data Mining (DM). In general, DM refers to the analysis of observational datasets
to extract useful information and to summarise the data so that it can be more
understandable and be used more efficiently in terms of storage and processing.
FS is the technique of selecting a subset of features that are relevant to the
development of learning models. Regression is the process of modelling and
identifying the possible relationships between groups of features (variables).
Comparing with the conventional techniques, Intelligent System Techniques
(ISTs) are usually favourable due to their flexible capabilities for handling real‐life
problems and the tolerance to data imprecision, uncertainty, partial truth, etc.
This thesis introduces a novel hybrid intelligent technique, namely Sensitive
Genetic Neural Optimisation (SGNO), which is capable of reducing the
dimensionality of a dataset by identifying the most important group of features.
The capability of SGNO is evaluated with four practical applications in three
research areas, including plant science, civil engineering and economics.
SGNO is constructed using three key techniques, known as the core modules,
including Genetic Algorithm (GA), Neural Network (NN) and Sensitivity Analysis
(SA). The GA module controls the progress of the algorithm and employs the NN
module as its fitness function. The SA module quantifies the importance of each
available variable using the results generated in the GA module. The global
sensitivity scores of the variables are used determine the importance of the
variables. Variables of higher sensitivity scores are considered to be more important than the variables with lower sensitivity scores. After determining the
variables’ importance, the performance of SGNO is evaluated using the NN module
that takes various numbers of variables with the highest global sensitivity scores
as the inputs. In addition, the symbolic relationship between a group of variables
with the highest global sensitivity scores and the model output is discovered
using the Multiple‐Branch Encoded Genetic Programming (MBE‐GP).
A total of four datasets have been used to evaluate the performance of SGNO.
These datasets involve the prediction of short‐term greenhouse tomato yield,
prediction of longitudinal dispersion coefficients in natural rivers, prediction of
wave overtopping at coastal structures and the modelling of relationship between
the growth of industrial inputs and the growth of the gross industrial output.
SGNO was applied to all these datasets to explore its effectiveness of reducing the
dimensionality of the datasets. The performance of SGNO is benchmarked with
four dimensionality reduction techniques, including Backward Feature Selection
(BFS), Forward Feature Selection (FFS), Principal Component Analysis (PCA) and
Genetic Neural Mathematical Method (GNMM).
The applications of SGNO on these datasets showed that SGNO is capable of
identifying the most important feature groups of in the datasets effectively and
the general performance of SGNO is better than those benchmarking techniques.
Furthermore, the symbolic relationships discovered using MBE‐GP can generate
performance competitive to the performance of NN models in terms of regression
accuracies
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