Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects

Gómez-Ossa, Luisa Fernanda; Botero-Fernández, Verónica

doi:10.15446/dyna.v84n201.54310

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…A sort of supervised network is the BP network model. When data is entered into the network, it is first transferred from the input layer to the hidden layer node, then sent to the next hidden layer following the characteristic function, and ultimately passed to the output layer [13]. e neural unit's function is usually an s-type function:…”

Section: Bp Nn and Algorithmmentioning

confidence: 99%

Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment

Kong¹

2022

Security and Communication Networks

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From the environmental and security status quo faced by big data, although big data is unstructured or more difficult to filter and analyze, it does not mean that big data is necessarily more secure. In order to be invincible in the fierce market competition, an enterprise must conduct an in-depth understanding and investigation of the rapid changes in the market and economic development. The current era is already the Internet era. The arrival of the era of network communications has greatly facilitated people’s lives. However, how to seize the opportunities in the era of network communication and make economic predictions based on actual situations are particularly important for enterprises. Therefore, we are required to make reasonable economic predictions of network communication services in order to seize opportunities and meet challenges. By analyzing the development and application of artificial neural network (NN), this article briefly introduces its development and principles. Based on the reality of a network communication industrial enterprise, it uses data modeling and comparative analysis to establish a logistic regression model, a decision tree model, and a BP NN. The model compares the customer types and data predictions of the enterprise’s data traffic under the three models. From the results of the model analysis, it can be seen that in the analysis of the three models, the ROC curve analysis, the BP NN predicts that the cumulative hit rate in the ROC curve is wider. More users who can handle traffic services will be covered. In the cumulative customer lift analysis, under the first set of data (that is, when the depth is 20), the cumulative depths of the decision tree model, logic analysis model, and BP neural system prediction model are 2.5, 3.4, and 3.6, respectively. The BP neural system prediction model has the highest value of cumulative depth. In the cumulative capture response percentage analysis, the cumulative capture response percentage from low to low is the decision tree model and logic analysis. From BP NN prediction model, we can draw the conclusions. The network system can play a good role in forecasting and help enterprise managers make economic decisions.

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Section: Bp Nn and Algorithmmentioning

confidence: 99%

Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment

Kong¹

2022

Security and Communication Networks

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“…The distance simulation to the forests shows that the protected forest for which there is a strong probability of the deforestation is associated to the conception of an asphalted road. These reports have has been obtained in Latina America Forest by Freitas et al [46] and by Gómez-Ossa & Botero-Fernández [45]. If the roads building constitutes by the facilitation access to the forests (a conservative action of the protected forests), it disturbs in this sense that the ecological studies suggest that the tropical forest overflows a biodiversity.…”

Section: ) Human Activities and Forest Cover Dynamicmentioning

confidence: 90%

“…This method was commonly used to determine and include/understand the anthropogenic action or deforestation causes. Moreover, an Euclidian distance analysis is the best indicator suited to represent deforestation [45,46]. One variable, like an asphalted national road named "La Côtière", has been used to explain the closed forests (FD) occurrence.…”

Section: D) Anthropogenic Activities and Road Building Analysis On Forest Degradationmentioning

confidence: 99%

Farmers Establishment Impact on the Forest Dynamic of Monogaga Protected Forest, in the Southwest of Côte d’Ivoire: Remote Sensing and Geographical Information Systems (GIS) Approach

Douffi¹,

Koné²,

Kouassi³

et al. 2019

EJERS

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The deforestation is a major problem in Côte d’Ivoire. Monogaga protected forest is strongly cleared for agriculture. Thus, the research aims to determine the influence of the farmer’s occupancies on the forest dynamic. The supervised classification by Neural net has been applied with success to Landsat imagery of 1986 and 2017. It has been preceded by the vegetation physiognomic characterization. The vegetation units are the closed forest, the degraded forest and the fallow-crops. The neural Net classification has been used to discriminate the closed forest area. To the dynamic level, the loss of closed forest proportion between 1986 and 2017 is 86.52%. The annual deforestation rate in regular (linear) and irregular (exponential) level is respectively 2.88% and 6.46% on 30 years. The anthropogenic units descended of the degradation represent some more 90 % of land cover, with a contribution of the crops at more than 40%. These same units are also implied in regeneration to a proportion of some more 90%, always with the crops proportion of 60%. Several mutations are in use. It’s the degradation, conservation and regeneration, with a predominance of degradation. The result shows the bad conservation. This survey showed that the protected forest is submitted to agricultural pressure and all other anthropogenic activities. It is important to have a forest conservation politics and more efficient management.

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“…In the department, new road infrastructure projects have been developed, aiming to enhance connectivity with the north of the country [72]. Additionally, it is the leading gold producer in Colombia [73].…”

Section: Study Area Definitionmentioning

confidence: 99%

Land Cover Classification in the Antioquia Region of the Tropical Andes Using NICFI Satellite Data Program Imagery and Semantic Segmentation Techniques

Gomez-Ossa,

Sanchez-Torres,

Branch-Bedoya

2023

Data

Self Cite

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Land cover classification, generated from satellite imagery through semantic segmentation, has become fundamental for monitoring land use and land cover change (LULCC). The tropical Andes territory provides opportunities due to its significance in the provision of ecosystem services. However, the lack of reliable data for this region, coupled with challenges arising from its mountainous topography and diverse ecosystems, hinders the description of its coverage. Therefore, this research proposes the Tropical Andes Land Cover Dataset (TALANDCOVER). It is constructed from three sample strategies: aleatory, minimum 50%, and 70% of representation per class, which address imbalanced geographic data. Additionally, the U-Net deep learning model is applied for enhanced and tailored classification of land covers. Using high-resolution data from the NICFI program, our analysis focuses on the Department of Antioquia in Colombia. The TALANDCOVER dataset, presented in TIF format, comprises multiband R-G-B-NIR images paired with six labels (dense forest, grasslands, heterogeneous agricultural areas, bodies of water, built-up areas, and bare-degraded lands) with an estimated 0.76 F1 score compared to ground truth data by expert knowledge and surpassing the precision of existing global cover maps for the study area. To the best of our knowledge, this work is a pioneer in its release of open-source data for segmenting coverages with pixel-wise labeled NICFI imagery at a 4.77 m resolution. The experiments carried out with the application of the sample strategies and models show F1 score values of 0.70, 0.72, and 0.74 for aleatory, balanced 50%, and balanced 70%, respectively, over the expert segmented sample (ground truth), which suggests that the personalized application of our deep learning model, together with the TALANDCOVER dataset offers different possibilities that facilitate the training of deep architectures for the classification of large-scale covers in complex areas, such as the tropical Andes. This advance has significant potential for decision making, emphasizing sustainable land use and the conservation of natural resources.

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Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects

Cited by 6 publications

References 26 publications

Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment

Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment

Farmers Establishment Impact on the Forest Dynamic of Monogaga Protected Forest, in the Southwest of Côte d’Ivoire: Remote Sensing and Geographical Information Systems (GIS) Approach

Land Cover Classification in the Antioquia Region of the Tropical Andes Using NICFI Satellite Data Program Imagery and Semantic Segmentation Techniques

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