scholarly journals Using GIS and Artificial Neural Network for Deforestation Prediction

2018 ◽  
Author(s):  
Vahid Ahmadi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Satellite Images ◽  
Learning Algorithm ◽  
Geospatial Information ◽  
Environmental Diversity ◽  
Artificial Neural ◽  
Main Components

Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

scholarly journals Using GIS and Artificial Neural Network for Deforestation Prediction

2018 ◽  
Author(s):  
Vahid Ahmadi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Satellite Images ◽  
Learning Algorithm ◽  
Geospatial Information ◽  
Environmental Diversity ◽  
Artificial Neural ◽  
Main Components

Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

scholarly journals ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR

2017 ◽  
Vol 12 ◽  
pp. 99
Author(s):  
Martin Ruzek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Human Operator ◽  
Biological Neural Network ◽  
Artificial Learning ◽  
Artificial Neural

This paper presents a new approach to mental functions modeling with the use of artificial neural networks. The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly inspired by the biological neuron. On the other hand, the classical paradigms of artificial neural networks are not suitable because they simplify too much the real processes in biological neural network. The search for a compromise between the complexity of biological neural network and the practical feasibility of the artificial network led to a new learning algorithm. This algorithm is based on the classical multilayered neural network; however, the learning rule is different. The neurons are updating their parameters in a way that is similar to real biological processes. The basic idea is that the neurons are competing for resources and the criterion to decide which neuron will survive is the usefulness of the neuron to the whole neural network. The neuron is not using "teacher" or any kind of superior system, the neuron receives only the information that is present in the biological system. The learning process can be seen as searching of some equilibrium point that is equal to a state with maximal importance of the neuron for the neural network. This position can change if the environment changes. The name of this type of learning, the homeostatic artificial neural network, originates from this idea, as it is similar to the process of homeostasis known in any living cell. The simulation results suggest that this type of learning can be useful also in other tasks of artificial learning and recognition.

scholarly journals TIME SERIES FORECASTING USING ARTIFICIAL NEURAL NETWORK WITH EXTENDED ADAPTIVE LEARNING

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Mr. Dhanaji Vilas Mirajkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Learning Rate ◽  
Experimental Result ◽  
Adaptive Learning Rate ◽  
Artificial Neural

Artificial neural network (ANN) mainly consists of learning algorithms, which are require to optimize the convergence of neural networks. We need to optimize the convergence of neural networks in order to improve the speed and accuracy of decision making process. To enable the optimization process one of the widely used algorithm is back propagation learning algorithm. Objective of study is to applied backpropagation algorithm for solving multivariate time series problem. To better the accuracy of neural network it is important to find optimized architecture for the problem under consideration. The learning rate is also an important factor which affects the performance of result. In this study, we proposed extended adaptive learning approach in which learning rate is adapted from number of previous iteration error trend in first half of training. In next half of training learning rate is adapted as per adaptive learning rate algorithm. Compare performance of three variation of backpropagationalgorithm. All these variation experimented on two standard dataset. Experimental result shows that during validation and training ANN with extended adaptive learning rate outperforms other than two variations.

scholarly journals TRANSFORMATION OF AN INFERENCE TREE TO A LOGICALLY EQUIVALENT NEURAL NETWORK

1997 ◽  
Vol 1 ◽  
pp. 67
Author(s):  
A. Molotkov ◽  
A. Borisov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Logical Structure ◽  
Acquisition Task ◽  
Network Application ◽  
Artificial Neural ◽  
Quantitative Evaluations ◽  
Inference Tree

III-formalized situations can easily be represented by means of conditional rules. Heuristics that are represented through rules well describe the relationships among the factors of the process or event to be examined. , .In such a situation two tasks should usually be solved. The first deals with constructing the logical structure of a set of rules which properly represent the described process. The second task is the adjustment of this structure on the level of quantitative evaluations which characterize the described relationships. A logically full, consistent and non-exhaustive set of rules is adjusted through learning procedures taken from the apparatus of artificial neural networks (Zurada 1992).When implementing this approch, the following tasks arise that are considered in the present:(i) construction of the adequate neuronlike structure,(ii) choice and implementation of a learning algorithm for training the above structure.Other investigations are also known that have used a neural network as an element of an expert system. Gallant (1986) proposes the methodology of artificial neural network application for the task of knowledge acquisition task.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Application of artificial neural networks to predict the deflections of reinforced concrete beams

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
The Neural Network ◽  
Eurocode 2 ◽  
Artificial Neural ◽  
Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

STRUCTURE OF THE DECISION SUPPORT INFORMATION SYSTEM BY THE NPP OPERATOR

2022 ◽  
pp. 8-12
Author(s):  
М.Е. Ушков ◽  
В.Л. Бурковский
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Support System ◽  
Control Object ◽  
Information Support ◽  
Quality Analysis ◽  
Inference System ◽  
Artificial Neural ◽  
Information Support System

Рассматривается структура системы информационной поддержки процессов принятия решений оператором АЭС в оперативных условиях. Анализируются функциональные возможности системы информационной поддержки оператора (СИПО) на примере Нововоронежской атомной электростанции (НВ АЭС). Данная система дает возможность оператору, управляющему распределенным комплексом технологических объектов АЭС, проводить качественный анализ и обработку больших объемов сложностpуктурированной информации и принимать своевременные адекватные решения в темпе реального времени. Кроме того, рассматривается объект управления и его структура, приводятся рекомендации, направленные на увеличение функциональных возможностей СИПО на базе искусственных нейронных сетей. Одной из многочисленных функций СИПО является прогнозирование состояния объекта управления на основе реализации программно-технологического комплекса модели энергоблока (ПТК МЭ). Однако существующая модель не способна учесть все факторы, влияющие на производственный процесс. Альтернативой здесь выступает искусственная нейронная сеть, которая в процессе обучения может сформировать искомые зависимости между большим числом параметров объекта управления и получить более полный и достоверный прогноз. Предложена структура искусственной нейронной сети на базе нечёткой системы вывода, которая реализует возможности нейронных сетей и нечеткой логики We considered the structure of the information support system for decision-making by the NPP operator in operational conditions. We analyzed the functional capabilities of the operator information support system (SIPO) using the example of the Novovoronezh nuclear power plant (NV NPP). This system provides the operator managing the distributed complex of NPP technological facilities to carry out high-quality analysis and processing of large volumes of complex structured information and make timely adequate decisions in real time. In addition, we considered the control object and its structure and made recommendations aimed at increasing the functionality of the SIPO based on artificial neural networks. One of the many functions of the SIPO is to predict the state of the control object based on the implementation of the software and technological complex of the power unit model. However, the existing model is not able to take into account all the factors influencing the production process. An alternative here is an artificial neural network, which in the learning process can form the required dependencies between a large number of parameters of the control object and get a more complete and reliable forecast. The proposed structure of an artificial neural network based on a fuzzy inference system, which implements the capabilities of neural networks and fuzzy logic

Sign in / Sign up

Export Citation Format

Share Document