Artificial Condition Pdf Free Download Adobe Reader
Artificial Condition Pdf Free Download Windows 10

Artificial Intelligence textbook pdf free download Artificial Intelligence textbook pdf free download. Artificial Intelligence is one of the famous textbook for engineering students. This book is mainly useful for Computer Science Engineering Students. This textbook is also useful to most of the students who were prepared for competitive exams. The author’s of this book clearly.
Mar 02, 2019 New Artificial Condition: The Murderbot Diaries EPUB PDF Download Read Martha Wells - Downloading to Kindle - Download to iPad/iPhone/iOS or Download to B&N nook. Reviews in epub, pdf and mobi formats.
Download Inteligencia Artificial.

Introduction to Artificial Intelligence PDF SLIDESIntroduction to Artificial Intelligence PDF SLIDESEngineering books free download,all PDF books free download with just one click,islamic books.

Artificial Condition Pdf Free Download Adobe Reader

Artificial Condition Pdf Free Download Windows 10

Journal of Environmental Management 238 (2019) 224–234
Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Research article
Reconstructing pre-ﬁre vegetation condition in the wildland urban interface (WUI) using artiﬁcial neural network
T
Maria Polinova∗, Lea Wittenberg, Haim Kutiel, Anna Brook Department of Geography and Environmental Studies, University of Haifa, Mount Carmel, 3498838, Israel
A R T I C LE I N FO
A B S T R A C T
Keywords: Pre-ﬁre conditions Artiﬁcial neural network Satellite imagery Urban vegetation
Wildﬁres occurring near and within cities are a potential threat to the population's life and health and can cause signiﬁcant economic damage by destroying infrastructure and private property. Due to the relatively small area of these wildlands, the accuracy of ﬁre risk-assessment plays a signiﬁcant role in ﬁre management. Introducing the experience of real events can improve accuracy. But this approach is limited by a lack of knowledge of preﬁre conditions, mainly vegetation characteristics as related to their deﬁnition as a fuel parameter because of their high temporal variation. To solve this problem, an Artiﬁcial Neural Network (ANN) was designed to reconstruct the spectral characteristics of the vegetation just before the ﬁre with spatial resolution 0.5–2 m from the Landsat image. To test the eﬀectiveness of the proposed methods, the approach has been examined on urban vegetation sites and applied to restore spectral information of the actual vegetation patch before it was burned in 2016 in Haifa, Israel. The results show that the reconstructed RGB image allows for mapping the location of green vegetation with high spatial accuracy. However, spectral data in the visible range have some limitations when it comes to identifying diﬀerences between soil and dry plants. The reconstructed image was used to sharp the original data from Landsat. Normalized Diﬀerence Vegetation Index maps were produced from the resulting high-resolution multispectral image. The output maps allow to determine the location of vegetation and estimate the level of its dryness on the urban wildland landscape. The proposed method aims to estimate vegetation dryness and, as a result, identify the fuel characteristics at the time of the ﬁre. It has the potential of using for evaluation and improve the weights of the input parameters for the ﬁre-risk assessment and ﬁre-behavior modeling on a speciﬁc area.
1. Introduction Wildﬁres are a natural process, aﬀecting environmental systems and have widespread eﬀects (FAO, 2007). While ﬁres are natural disturbances and some communities and certain species depend on them, the threat to life, economic losses, and environmental damage associated with wildﬁre forces authorities to devote signiﬁcant eﬀort to ﬁre management through their prevention, detection, control, restriction, and suppression (FAO, 2006). In some regions, the negative eﬀects of ﬁres are so important that ﬁre safety is receiving increased attention. For example, in the Euro-Mediterranean region, wildﬁres burn nearly 450,000 ha each year, which has led to active work on the development and improvement of ﬁre prevention and ﬁghting approaches (Fox et al., 2016). Despite all the ﬁre management treatments, wildﬁres are expected to further increase in these regions as a result of climate change and intensiﬁed land-use practices (Levin et al., 2016) e.g. open
agricultural burning or urban sprawl which increases the anthropogenic load on the nearby wildlands (Scott et al., 2013). Of particular importance is ﬁre prevention activity in the areas adjacent to places of human habitation. Since anthropogenic activity near or directly on the natural landscape increases the ﬁre risk (Assaker and Darwish, 2012). This challenge is especially acute in the Mediterranean region, where, wildﬁres in urban environments become a frequent occurrence (Darques, 2015). One of the important components of ﬁre management is the analysis of ﬁre risks. Nowadays, three model types and their combinations for ﬁre risk assessment have been proven to be eﬀective: biophysical models that predict ﬁre risks from vegetation type, fuel load, and climate; statistical models; and ﬁre behavior models (Weinstein and Woodbury, 2010). Most of these models use data on fuel, or rather the characteristics of the vegetation, as the main drivers that determine the probability of ignition. Data collection and pre-processing for
∗
Corresponding authorDepartment of Geography and Environmental Studies, University of Haifa, Mount Carmel, 3498838, Israel. E-mail addresses: [email protected]ﬀ.haifa.ac.il (M. Polinova), [email protected] (L. Wittenberg), [email protected] (H. Kutiel), [email protected] (A. Brook). https://doi.org/10.1016/j.jenvman.2019.02.091 Received 6 July 2018; Received in revised form 12 February 2019; Accepted 19 February 2019 0301-4797/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Environmental Management 238 (2019) 224–234
M. Polinova, et al.
method already has shortcomings when changing pixel size by 6–8 times (Chen et al., 2018). The disadvantage relies on the principle of computer vision used in CNN: reduced more than 4 times spatial resolution does not allow the computer to perceive information from the image. The alternative approach suitable for reconstruction tasks is an artiﬁcial neural network (ANN), which is proposed to estimate nonlinear relations between a data set of numeric inputs and a set of numeric targets (Raﬁq et al., 2001). In the case of reconstructing vegetation spectrum with a high spatial resolution from satellite imagery, an ANN can be a more promising tool since it considers images as a matrix of data and estimates a ﬁtting function regardless the quality of the original image. The aim of this study is to reconstruct the spatial and spectral properties of vegetation before the ﬁre event using the example of Haifa's wildﬁre in November 2016. For this, an ANN was developed and trained to reconstruct the RGB image with the spatial resolution 0.5–2.0 m based on visible bands of Landsat spectral images and orthophoto obtained from the unmanned aerial vehicle (UAV). Pre-ﬁre vegetation conditions reconstruction provide new opportunities to study ﬁre's spatio-temporal dynamic and patterns in urban area. The results of the study may contribute to better terrain planning, prevention activities, preparation of the population for extreme situations and wildﬁre ﬁghting in urban areas. Since ﬁre is a common and signiﬁcant disturbance, such knowledge is of great value for ﬁre management in urban areas and ﬁre connectivity analysis.
introducing vegetation parameters into risk assessment models is a well-studied topic. The most widespread approach is analysis of vegetation characteristics for classiﬁcation into stan

Artificial Condition PDF Free Download