Alfred Homère Ngandam Mfondoum, Pauline Wokwenmendam Nguet, Dieudonné Tchokona Seuwui, Jean Valéry Mefire Mfondoum, Henry Bang Ngenyam, Ibrahima Diba, Mesmin Tchindjang, Bertin Djiangoue, Ali Mihi, Sofia Hakdaoui, Roseline Batcha, Frédéric Chamberlain Lounang Tchatchouang, Igor Casimir Njombissie Petcheu, Luc Moutila Beni
{"title":"将层次分析法与机器学习算法逐步整合,用于喀麦隆北蒙戈周边地区的滑坡、沟壑侵蚀和山洪易感性测绘","authors":"Alfred Homère Ngandam Mfondoum, Pauline Wokwenmendam Nguet, Dieudonné Tchokona Seuwui, Jean Valéry Mefire Mfondoum, Henry Bang Ngenyam, Ibrahima Diba, Mesmin Tchindjang, Bertin Djiangoue, Ali Mihi, Sofia Hakdaoui, Roseline Batcha, Frédéric Chamberlain Lounang Tchatchouang, Igor Casimir Njombissie Petcheu, Luc Moutila Beni","doi":"10.1186/s40677-023-00254-5","DOIUrl":null,"url":null,"abstract":"Abstract Background The Cameroon Volcanic Line (CVL) is an oceanic-continental megastructure prone to geo-hazards, including landslide/mudslide, gully erosion and flash floods targeted in this paper. Recent geospatial practices advocated a multi-hazard analysis approach supported by artificial intelligence. This study proposes the Multi-Geoenvironmental Hazards Susceptibility (MGHS) tool, by combining Analytical Hierarchy Process (AHP) with Machine Learning (ML) over the North-Moungo perimeter (Littoral Region, Cameroon). Methods Twenty-four factors were constructed from satellite imagery, global geodatabase and fieldwork data. Multicollinearity among these factors was quantified using the tolerance coefficient (TOL) and variance inflation factor (VIF). The AHP coefficients were used to weigh the factors and produce a preliminary map per Geoenvironmental hazard through weighted linear combination (WLC). The sampling was conducted based on events records and analyst knowledge to proceed with classification using Google Earth Engine (GEE) cloud computing interface. Classification and Regression Trees (CART), Random Forest (RF) and Gradient Boosting Regression Trees (GBRT), were used as basic learners of the stacked hazard factors, whereas, Support Vector Regression (SVR), was used for a meta-learning. Results The rainfall was ranked as the highest triggering factor for all Geoenvironmental hazards according to AHP, with a coefficient of 1 , while the after-learning importance assessment was more varied. The area under receiver operating characteristic (AUROC/AUC) was always more than 0.96 , and F 1 -score is between [ 0.86–0.88 ] for basic classifiers. Landslides, gully erosion and flash floods showed different spatial distributions, confirming then their probability of co-occurrence. MGHS outputs clearly displayed two and three simultaneous occurrences. Finally, the human vulnerability assessed with population layer and SVR outputs showed that high human concentrations are also the most exposed, using the example of Nkongsamba’s extract. Conclusions Combining AHP with single learners, then a meta-learner, was efficient in modelling MGHS and related human vulnerability. Interactions among geo-environmental hazards are the next step and city councils are recommended to integrate results in the planning process.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"140 1","pages":"0"},"PeriodicalIF":3.8000,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Stepwise integration of analytical hierarchy process with machine learning algorithms for landslide, gully erosion and flash flood susceptibility mapping over the North-Moungo perimeter, Cameroon\",\"authors\":\"Alfred Homère Ngandam Mfondoum, Pauline Wokwenmendam Nguet, Dieudonné Tchokona Seuwui, Jean Valéry Mefire Mfondoum, Henry Bang Ngenyam, Ibrahima Diba, Mesmin Tchindjang, Bertin Djiangoue, Ali Mihi, Sofia Hakdaoui, Roseline Batcha, Frédéric Chamberlain Lounang Tchatchouang, Igor Casimir Njombissie Petcheu, Luc Moutila Beni\",\"doi\":\"10.1186/s40677-023-00254-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Background The Cameroon Volcanic Line (CVL) is an oceanic-continental megastructure prone to geo-hazards, including landslide/mudslide, gully erosion and flash floods targeted in this paper. Recent geospatial practices advocated a multi-hazard analysis approach supported by artificial intelligence. This study proposes the Multi-Geoenvironmental Hazards Susceptibility (MGHS) tool, by combining Analytical Hierarchy Process (AHP) with Machine Learning (ML) over the North-Moungo perimeter (Littoral Region, Cameroon). Methods Twenty-four factors were constructed from satellite imagery, global geodatabase and fieldwork data. Multicollinearity among these factors was quantified using the tolerance coefficient (TOL) and variance inflation factor (VIF). The AHP coefficients were used to weigh the factors and produce a preliminary map per Geoenvironmental hazard through weighted linear combination (WLC). The sampling was conducted based on events records and analyst knowledge to proceed with classification using Google Earth Engine (GEE) cloud computing interface. Classification and Regression Trees (CART), Random Forest (RF) and Gradient Boosting Regression Trees (GBRT), were used as basic learners of the stacked hazard factors, whereas, Support Vector Regression (SVR), was used for a meta-learning. Results The rainfall was ranked as the highest triggering factor for all Geoenvironmental hazards according to AHP, with a coefficient of 1 , while the after-learning importance assessment was more varied. The area under receiver operating characteristic (AUROC/AUC) was always more than 0.96 , and F 1 -score is between [ 0.86–0.88 ] for basic classifiers. Landslides, gully erosion and flash floods showed different spatial distributions, confirming then their probability of co-occurrence. MGHS outputs clearly displayed two and three simultaneous occurrences. Finally, the human vulnerability assessed with population layer and SVR outputs showed that high human concentrations are also the most exposed, using the example of Nkongsamba’s extract. Conclusions Combining AHP with single learners, then a meta-learner, was efficient in modelling MGHS and related human vulnerability. Interactions among geo-environmental hazards are the next step and city councils are recommended to integrate results in the planning process.\",\"PeriodicalId\":37025,\"journal\":{\"name\":\"Geoenvironmental Disasters\",\"volume\":\"140 1\",\"pages\":\"0\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2023-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoenvironmental Disasters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s40677-023-00254-5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoenvironmental Disasters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40677-023-00254-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Stepwise integration of analytical hierarchy process with machine learning algorithms for landslide, gully erosion and flash flood susceptibility mapping over the North-Moungo perimeter, Cameroon
Abstract Background The Cameroon Volcanic Line (CVL) is an oceanic-continental megastructure prone to geo-hazards, including landslide/mudslide, gully erosion and flash floods targeted in this paper. Recent geospatial practices advocated a multi-hazard analysis approach supported by artificial intelligence. This study proposes the Multi-Geoenvironmental Hazards Susceptibility (MGHS) tool, by combining Analytical Hierarchy Process (AHP) with Machine Learning (ML) over the North-Moungo perimeter (Littoral Region, Cameroon). Methods Twenty-four factors were constructed from satellite imagery, global geodatabase and fieldwork data. Multicollinearity among these factors was quantified using the tolerance coefficient (TOL) and variance inflation factor (VIF). The AHP coefficients were used to weigh the factors and produce a preliminary map per Geoenvironmental hazard through weighted linear combination (WLC). The sampling was conducted based on events records and analyst knowledge to proceed with classification using Google Earth Engine (GEE) cloud computing interface. Classification and Regression Trees (CART), Random Forest (RF) and Gradient Boosting Regression Trees (GBRT), were used as basic learners of the stacked hazard factors, whereas, Support Vector Regression (SVR), was used for a meta-learning. Results The rainfall was ranked as the highest triggering factor for all Geoenvironmental hazards according to AHP, with a coefficient of 1 , while the after-learning importance assessment was more varied. The area under receiver operating characteristic (AUROC/AUC) was always more than 0.96 , and F 1 -score is between [ 0.86–0.88 ] for basic classifiers. Landslides, gully erosion and flash floods showed different spatial distributions, confirming then their probability of co-occurrence. MGHS outputs clearly displayed two and three simultaneous occurrences. Finally, the human vulnerability assessed with population layer and SVR outputs showed that high human concentrations are also the most exposed, using the example of Nkongsamba’s extract. Conclusions Combining AHP with single learners, then a meta-learner, was efficient in modelling MGHS and related human vulnerability. Interactions among geo-environmental hazards are the next step and city councils are recommended to integrate results in the planning process.
期刊介绍:
Geoenvironmental Disasters is an international journal with a focus on multi-disciplinary applied and fundamental research and the effects and impacts on infrastructure, society and the environment of geoenvironmental disasters triggered by various types of geo-hazards (e.g. earthquakes, volcanic activity, landslides, tsunamis, intensive erosion and hydro-meteorological events).
The integrated study of Geoenvironmental Disasters is an emerging and composite field of research interfacing with areas traditionally within civil engineering, earth sciences, atmospheric sciences and the life sciences. It centers on the interactions within and between the Earth''s ground, air and water environments, all of which are affected by climate, geological, morphological and anthropological processes; and biological and ecological cycles. Disasters are dynamic forces which can change the Earth pervasively, rapidly, or abruptly, and which can generate lasting effects on the natural and built environments.
The journal publishes research papers, case studies and quick reports of recent geoenvironmental disasters, review papers and technical reports of various geoenvironmental disaster-related case studies. The focus on case studies and quick reports of recent geoenvironmental disasters helps to advance the practical understanding of geoenvironmental disasters and to inform future research priorities; they are a major component of the journal. The journal aims for the rapid publication of research papers at a high scientific level. The journal welcomes proposals for special issues reflecting the trends in geoenvironmental disaster reduction and monothematic issues. Researchers and practitioners are encouraged to submit original, unpublished contributions.