Ecol. Informatics最新文献_第5页

A review of the quality of environmental impact statements with a focus on urban projects from Romania 审查环境影响说明的质量，重点是罗马尼亚的城市项目

Ecol. Informatics Pub Date : 2022-06-01 DOI: 10.1016/j.ecoinf.2022.101723

Andreea Niță, C. Hossu, Cristina G. Mitincu, I. Ioja

引用次数: 7

A MaxEnt modelling approach to understand the climate change effects on the distributional range of White-bellied Sholakili Sholicola albiventris (Blanford, 1868) in the Western Ghats, India MaxEnt造型方法了解气候变化影响的分布范围White-bellied Sholakili Sholicola albiventris(布兰福德,1868)在西高止山脉,印度

Ecol. Informatics Pub Date : 2022-06-01 DOI: 10.1016/j.ecoinf.2022.101702

E. R. Sreekumar, P. O. Nameer

引用次数: 5

Prospects for the monitoring of the great cormorant (Phalacrocorax carbo sinensis) using a drone and stationary cameras 无人机和固定式摄像机对大鸬鹚监测的展望

Ecol. Informatics Pub Date : 2022-06-01 DOI: 10.1016/j.ecoinf.2022.101726

Jakub Polenský, J. Regenda, Z. Adámek, P. Císar̆

引用次数: 2

Pollution characteristics and risk assessment of heavy metals in the soil of a construction waste landfill site 某建筑垃圾填埋场土壤重金属污染特征及风险评价

Ecol. Informatics Pub Date : 2022-06-01 DOI: 10.1016/j.ecoinf.2022.101700

Gaofeng Wu, Lili Wang, R. Yang, Wenxing Hou, Shanwen Zhang, Xiaoyu Guo, Wenji Zhao

引用次数: 22

New generation neurocomputing learning coupled with a hybrid neuro-fuzzy model for quantifying water quality index variable: A case study from Saudi Arabia 新一代神经计算学习结合混合神经模糊模型量化水质指标变量:以沙特阿拉伯为例

Ecol. Informatics Pub Date : 2022-05-01 DOI: 10.1016/j.ecoinf.2022.101696

M. S. Manzar, M. Benaafi, R. Costache, O. Alagha, N. Mu’azu, M. Zubair, J. Abdullahi, S. Abba

引用次数: 22

A method for automatic real-time detection and counting of fruit fly pests in orchards by trap bottles via convolutional neural network with attention mechanism added 引入注意机制的卷积神经网络陷阱瓶对果园害虫进行实时自动检测与计数

Ecol. Informatics Pub Date : 2022-05-01 DOI: 10.1016/j.ecoinf.2022.101690

Jinhui She, Wei Zhan, Shengbing Hong, Chao Min, Tianyu Dong, Huazi Huang, Zhangzhang He

引用次数: 18

Alpine treelines as ecological indicators of global climate change: Who has studied? What has been studied? 高山树线作为全球气候变化的生态指标:谁研究过?研究了什么?

Ecol. Informatics Pub Date : 2022-05-01 DOI: 10.1016/j.ecoinf.2022.101691

Wensheng Chen, Huihui Ding, Jiang Li, Kan-Fan Chen, Hanju Wang

引用次数: 8

Semantic Sensor Network Ontology based Decision Support System for Forest Fire Management 基于语义传感器网络本体的森林火灾管理决策支持系统

Ecol. Informatics Pub Date : 2022-04-03 DOI: 10.48550/arXiv.2204.03059

Ritesh Chandra, Sonali Agarwal, Navjot Singh

{"title":"Semantic Sensor Network Ontology based Decision Support System for Forest Fire Management","authors":"Ritesh Chandra, Sonali Agarwal, Navjot Singh","doi":"10.48550/arXiv.2204.03059","DOIUrl":"https://doi.org/10.48550/arXiv.2204.03059","url":null,"abstract":"The forests are significant assets for every country. When it gets destroyed, it may negatively impact the environment, and forest fire is one of the primary causes. Fire weather indices are widely used to measure fire danger and are used to issue bushfire warnings. It can also be used to predict the demand for emergency management resources. Sensor networks have grown in popularity in data collection and processing capabilities for a variety of applications in industries such as medical, environmental monitoring, home automation etc. Semantic sensor networks can collect various climatic circumstances like wind speed, temperature, and relative humidity. However, estimating fire weather indices is challenging due to the various issues involved in processing the data streams generated by the sensors. Hence, the importance of forest fire detection has increased day by day. The underlying Semantic Sensor Network (SSN) ontologies are built to allow developers to create rules for calculating fire weather indices and also the convert dataset into Resource Description Framework (RDF). This research describes the various steps involved in developing rules for calculating fire weather indices. Besides, this work presents a Web-based mapping interface to help users visualize the changes in fire weather indices over time. With the help of the inference rule, it designed a decision support system using the SSN ontology and query on it through SPARQL. The proposed fire management system acts according to the situation, supports reasoning and the general semantics of the open-world followed by all the ontologies.","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127953997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Predicting the probability of avian reproductive success and its components at a nesting site 预测鸟类在筑巢地点繁殖成功的概率及其组成部分

Ecol. Informatics Pub Date : 2022-02-22 DOI: 10.21203/rs.3.rs-1313546/v1

Sinchan Ghosh, A. Banerjee, Soumalya Mukhopadhyay, S. Bhattacharya, S. Ray

{"title":"Predicting the probability of avian reproductive success and its components at a nesting site","authors":"Sinchan Ghosh, A. Banerjee, Soumalya Mukhopadhyay, S. Bhattacharya, S. Ray","doi":"10.21203/rs.3.rs-1313546/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-1313546/v1","url":null,"abstract":"\u0000 Avian reproduction has three chronological components: nesting, mating, hatching, and fledging. Predicting the probability of individual components helps to identify the period of reproduction that needs the most aid, increasing the conservation efficiency. This prediction requires identification of biotic, abiotic, and sociological variables of a bird’s environment responsible for these componentwise success probabilities. There is also no standard methodology to estimate these probability values separately. This study estimates the absolute success probability of each component, identifies correlated environmental predictors and gives a modeling framework to accurately predict the success probabilities using Merops Philippines as a test bed. The result using surveyed data and proposed methodology indicates the corridor between nesting and mating is most vulnerable to the environment. Social structure is the key to all reproductive components but nesting. Both biotic and abiotic factors are crucial determinants of nesting success. Mating, hatching, and fledging success depend more on biotic factors than abiotic ones. Linear modeling frameworks are helpful to explore which types of environment are a better determinant of the success of a reproductive component. Artificial neural networking is more useful to predict the successes of a new site. Although developed using Merops philippinus data, the proposed methodology and modeling framework are also applicable for other birds.","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116797130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated distance estimation for wildlife camera trapping 自动距离估计野生动物相机陷阱

Ecol. Informatics Pub Date : 2022-02-09 DOI: 10.1016/j.ecoinf.2022.101734

Peter Johanns, T. Haucke, V. Steinhage

引用次数: 6