{"title":"用于预测斯里兰卡人象冲突风险潜在分布的 Maxent 模型","authors":"Sachini Jayakody , Ian Estacio , Corinthias P.M. Sianipar , Kenichiro Onitsuka , Mrittika Basu , Satoshi Hoshino","doi":"10.1016/j.apgeog.2024.103447","DOIUrl":null,"url":null,"abstract":"<div><div>Human-elephant conflict (HEC) is a major problem that causes loss of life to both humans and elephants. While HEC risk models have been developed in past studies, there has not been any HEC risk models developed for the country with the highest annual HEC-related elephant deaths which is Sri Lanka. Thus, this study aims to develop a nationwide model to predict the risk of HEC and identify the most significant predictor variables for HEC in Sri Lanka. HEC risk variables and thirteen predictor variables were prepared using GIS tools. The MaxEnt application was used to input the risk variables (as presence points) and predictor variables (as environmental layers) and model the probability of HEC risk at 500m resolution. The modeling showed that distance to elephant distribution areas was the most important predictor variable for HEC, followed by vegetation area, elevation, rangeland area, population density, and agricultural area. The results are supported by past studies that show the preference of elephants to remain within their usual range, but venturing out for food and water when resources are lacking. This is the first study to develop a nationwide HEC risk map for Sri Lanka using machine learning.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103447"},"PeriodicalIF":4.0000,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Maxent modeling for predicting the potential distribution of human-elephant conflict risk in Sri Lanka\",\"authors\":\"Sachini Jayakody , Ian Estacio , Corinthias P.M. Sianipar , Kenichiro Onitsuka , Mrittika Basu , Satoshi Hoshino\",\"doi\":\"10.1016/j.apgeog.2024.103447\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Human-elephant conflict (HEC) is a major problem that causes loss of life to both humans and elephants. While HEC risk models have been developed in past studies, there has not been any HEC risk models developed for the country with the highest annual HEC-related elephant deaths which is Sri Lanka. Thus, this study aims to develop a nationwide model to predict the risk of HEC and identify the most significant predictor variables for HEC in Sri Lanka. HEC risk variables and thirteen predictor variables were prepared using GIS tools. The MaxEnt application was used to input the risk variables (as presence points) and predictor variables (as environmental layers) and model the probability of HEC risk at 500m resolution. The modeling showed that distance to elephant distribution areas was the most important predictor variable for HEC, followed by vegetation area, elevation, rangeland area, population density, and agricultural area. The results are supported by past studies that show the preference of elephants to remain within their usual range, but venturing out for food and water when resources are lacking. This is the first study to develop a nationwide HEC risk map for Sri Lanka using machine learning.</div></div>\",\"PeriodicalId\":48396,\"journal\":{\"name\":\"Applied Geography\",\"volume\":\"173 \",\"pages\":\"Article 103447\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geography\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0143622824002522\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geography","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0143622824002522","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
Maxent modeling for predicting the potential distribution of human-elephant conflict risk in Sri Lanka
Human-elephant conflict (HEC) is a major problem that causes loss of life to both humans and elephants. While HEC risk models have been developed in past studies, there has not been any HEC risk models developed for the country with the highest annual HEC-related elephant deaths which is Sri Lanka. Thus, this study aims to develop a nationwide model to predict the risk of HEC and identify the most significant predictor variables for HEC in Sri Lanka. HEC risk variables and thirteen predictor variables were prepared using GIS tools. The MaxEnt application was used to input the risk variables (as presence points) and predictor variables (as environmental layers) and model the probability of HEC risk at 500m resolution. The modeling showed that distance to elephant distribution areas was the most important predictor variable for HEC, followed by vegetation area, elevation, rangeland area, population density, and agricultural area. The results are supported by past studies that show the preference of elephants to remain within their usual range, but venturing out for food and water when resources are lacking. This is the first study to develop a nationwide HEC risk map for Sri Lanka using machine learning.
期刊介绍:
Applied Geography is a journal devoted to the publication of research which utilizes geographic approaches (human, physical, nature-society and GIScience) to resolve human problems that have a spatial dimension. These problems may be related to the assessment, management and allocation of the world physical and/or human resources. The underlying rationale of the journal is that only through a clear understanding of the relevant societal, physical, and coupled natural-humans systems can we resolve such problems. Papers are invited on any theme involving the application of geographical theory and methodology in the resolution of human problems.