Pub Date : 2022-06-01DOI: 10.1016/j.ecoinf.2022.101723
Andreea Niță, C. Hossu, Cristina G. Mitincu, I. Ioja
{"title":"A review of the quality of environmental impact statements with a focus on urban projects from Romania","authors":"Andreea Niță, C. Hossu, Cristina G. Mitincu, I. Ioja","doi":"10.1016/j.ecoinf.2022.101723","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2022.101723","url":null,"abstract":"","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128082628","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}
Pub Date : 2022-06-01DOI: 10.1016/j.ecoinf.2022.101702
E. R. Sreekumar, P. O. Nameer
{"title":"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","authors":"E. R. Sreekumar, P. O. Nameer","doi":"10.1016/j.ecoinf.2022.101702","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2022.101702","url":null,"abstract":"","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120241168","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}
Pub Date : 2022-06-01DOI: 10.1016/j.ecoinf.2022.101726
Jakub Polenský, J. Regenda, Z. Adámek, P. Císar̆
{"title":"Prospects for the monitoring of the great cormorant (Phalacrocorax carbo sinensis) using a drone and stationary cameras","authors":"Jakub Polenský, J. Regenda, Z. Adámek, P. Císar̆","doi":"10.1016/j.ecoinf.2022.101726","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2022.101726","url":null,"abstract":"","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128080184","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}
{"title":"Pollution characteristics and risk assessment of heavy metals in the soil of a construction waste landfill site","authors":"Gaofeng Wu, Lili Wang, R. Yang, Wenxing Hou, Shanwen Zhang, Xiaoyu Guo, Wenji Zhao","doi":"10.1016/j.ecoinf.2022.101700","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2022.101700","url":null,"abstract":"","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125794284","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}
Pub Date : 2022-05-01DOI: 10.1016/j.ecoinf.2022.101696
M. S. Manzar, M. Benaafi, R. Costache, O. Alagha, N. Mu’azu, M. Zubair, J. Abdullahi, S. Abba
{"title":"New generation neurocomputing learning coupled with a hybrid neuro-fuzzy model for quantifying water quality index variable: A case study from Saudi Arabia","authors":"M. S. Manzar, M. Benaafi, R. Costache, O. Alagha, N. Mu’azu, M. Zubair, J. Abdullahi, S. Abba","doi":"10.1016/j.ecoinf.2022.101696","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2022.101696","url":null,"abstract":"","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124549008","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}
Pub Date : 2022-05-01DOI: 10.1016/j.ecoinf.2022.101691
Wensheng Chen, Huihui Ding, Jiang Li, Kan-Fan Chen, Hanju Wang
{"title":"Alpine treelines as ecological indicators of global climate change: Who has studied? What has been studied?","authors":"Wensheng Chen, Huihui Ding, Jiang Li, Kan-Fan Chen, Hanju Wang","doi":"10.1016/j.ecoinf.2022.101691","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2022.101691","url":null,"abstract":"","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124140285","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}
Pub Date : 2022-04-03DOI: 10.48550/arXiv.2204.03059
Ritesh Chandra, Sonali Agarwal, Navjot Singh
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.
{"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}
Pub Date : 2022-02-22DOI: 10.21203/rs.3.rs-1313546/v1
Sinchan Ghosh, A. Banerjee, Soumalya Mukhopadhyay, S. Bhattacharya, S. Ray
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.
{"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}
Pub Date : 2022-02-09DOI: 10.1016/j.ecoinf.2022.101734
Peter Johanns, T. Haucke, V. Steinhage
{"title":"Automated distance estimation for wildlife camera trapping","authors":"Peter Johanns, T. Haucke, V. Steinhage","doi":"10.1016/j.ecoinf.2022.101734","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2022.101734","url":null,"abstract":"","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126759864","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}
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