{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:36:12Z","timestamp":1773790572881,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41872253"],"award-info":[{"award-number":["41872253"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFB3900017"],"award-info":[{"award-number":["2022YFB3900017"]}]},{"name":"National Natural Science Foundation of China","award":["DD2021364"],"award-info":[{"award-number":["DD2021364"]}]},{"name":"National Key R &amp; D Program of China","award":["41872253"],"award-info":[{"award-number":["41872253"]}]},{"name":"National Key R &amp; D Program of China","award":["2022YFB3900017"],"award-info":[{"award-number":["2022YFB3900017"]}]},{"name":"National Key R &amp; D Program of China","award":["DD2021364"],"award-info":[{"award-number":["DD2021364"]}]},{"name":"China Geological Survey","award":["41872253"],"award-info":[{"award-number":["41872253"]}]},{"name":"China Geological Survey","award":["2022YFB3900017"],"award-info":[{"award-number":["2022YFB3900017"]}]},{"name":"China Geological Survey","award":["DD2021364"],"award-info":[{"award-number":["DD2021364"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["41872253"],"award-info":[{"award-number":["41872253"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2022YFB3900017"],"award-info":[{"award-number":["2022YFB3900017"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["DD2021364"],"award-info":[{"award-number":["DD2021364"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, Feature Engineering (FE) was applied to Landslide Susceptibility Mapping (LSM), while the most suitable conditioning feature dataset and analysis method were tested and analyzed. Tianshui city was taken as the study area, three types of geohazard (collapse, landslide, and unstable slopes) were used, while a total of twenty-three conditioning features were generated; two dimensionless methods (normalization and standardization) were tested afterward. Four Random-Forest-based (RF-based) feature selection methods using different indicators (Gini Impurity, GI; Out of Bag Accuracy, OOBA) were proposed and tested separately. The LSMs of four models were carried out under the guidance results of FE, namely Classification and Regression Tree (CART), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine for Classification (SVC). For feature enhancement, standardization had significant advantages over normalization. All RF-based methods were proven effective, lifting the AUC by 0.01~0.02. The RF model achieved the highest LSM accuracies, respectively, 0.949 (landslide), 0.957, and 0.949 (unstable slopes), improved by 0.008 (landslide), 0.005 (collapse), and 0.013 (unstable slopes). This proved that the FE helped to improve LSM and can help to decide the dominant conditioning factors for regional geohazards.<\/jats:p>","DOI":"10.3390\/rs14225658","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T02:07:48Z","timestamp":1668046068000},"page":"5658","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Feature Engineering of Geohazard Susceptibility Analysis Based on the Random Forest Algorithm: Taking Tianshui City, Gansu Province, as an Example"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiao","family":"Ling","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Yueqin","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management, Beijing 100085, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-7399","authenticated-orcid":false,"given":"Dongping","family":"Ming","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Yangyang","family":"Chen","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Tongyao","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Highland, L., and Bobrowsky, P.T. 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