利用卫星项圈数据预测亚洲象行为多样性的机器学习模型

Q4 Computer Science
Nurul Su'aidah Ahmad Radzali, A. Abu Bakar, Amri Izaffi Zamahsasri
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引用次数: 0

摘要

随着各种跟踪设备的发展和使用,使用统计应用程序和机器学习分析动物运动数据已经迅速发展。使用全球定位系统(GPS)收集时间和空间尺度上的位置和运动数据,以估计动物的位置。相比之下,安装卫星项圈可以确保持续监测,因为接收到的数据将直接发送到电子邮箱。然而,从卫星项圈数据中确定大象活动的确切模式仍然具有挑战性。本研究旨在提出一种机器学习模型来预测亚洲象的行为多样性。本研究包括四个主要阶段,包括两个层次的模型开发,以产生初始和初级分类模型。这些阶段是数据收集和准备、数据标记和初始分类模型开发、所有数据分类和初级分类模型开发。马来西亚野生动物和国家公园部从2018年至2020年在森林保护区的五头大象(三公两母)身上安装了卫星项圈,收集了大象行为数据。这项研究的结果是一个新的分类模型,可以预测亚洲象运动的行为。研究结果表明,XGBoost方法可以产生预测模型,以100%的准确率对亚洲象的行为进行分类。这项研究揭示了机器学习在确定行为类别和制定未来保护该物种的举措方面的决策能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Models for Behavioural Diversity of Asian Elephants Prediction Using Satellite Collar Data
Analysis of animal movement data using statistical applications and machine learning has developed rapidly in line with the developmentand use of various tracking devices. Location and movement data at temporal and spatial scales are collected using the Global PositioningSystem (GPS) to estimate the location of animals. In contrast, installing a satellite collar can ensure continuous monitoring, as the receiveddata will be sent directly to the electronic mailbox. Nevertheless, identifying an exact pattern of elephant activity from satellite collar data is still challenging. This study aimed to propose a machine learning model to predict the behavioural diversity of Asian elephants. The study involved four main phases, including two levels of model development, to produce initial and primary classification models. The phases were data collection and preparation, data labelling and initial classification model development, all data classification, and primary classification model development. The elephant behaviour data were collected from the satellite collars attached to five elephants, three males and two females, in forest reserves from 2018 to 2020 by the Department of Wildlife and National Parks, Malaysia. The study’s outcome was a novel classification model that can predict the behaviour of the Asian elephant movement. The findings showed that the XGBoost method could produce the predictive model to classify Asian elephants’ behaviour with 100 percent accuracy. This study revealed the capability of machine learning to identify behaviour classes and decision-making in setting initiatives to preserve this species in the future.
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
95
期刊介绍: IJICT is a refereed journal in the field of information and communication technology (ICT), providing an international forum for professionals, engineers and researchers. IJICT reports the new paradigms in this emerging field of technology and envisions the future developments in the frontier areas. The journal addresses issues for the vertical and horizontal applications in this area. Topics covered include: -Information theory/coding- Information/IT/network security, standards, applications- Internet/web based systems/products- Data mining/warehousing- Network planning, design, administration- Sensor/ad hoc networks- Human-computer intelligent interaction, AI- Computational linguistics, digital speech- Distributed/cooperative media- Interactive communication media/content- Social interaction, mobile communications- Signal representation/processing, image processing- Virtual reality, cyber law, e-governance- Microprocessor interfacing, hardware design- Control of industrial processes, ERP/CRM/SCM
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