Editing Landslide (section)

==Landslide prediction mapping==
{{see also|Slope stability analysis}}
Landslide hazard analysis and mapping can provide useful information for catastrophic loss reduction, and assist in the development of guidelines for sustainable [[land-use planning]]. The analysis is used to identify the factors that are related to landslides, estimate the relative contribution of factors causing slope failures, establish a relation between the factors and landslides, and to predict the landslide hazard in the future based on such a relationship.<ref>{{cite journal|last1=Chen|first1=Zhaohua|last2=Wang|first2=Jinfei|title=Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada|journal=Natural Hazards|volume=42|pages=75–89|year=2007|issue=1 |doi=10.1007/s11069-006-9061-6|bibcode=2007NatHa..42...75C |s2cid=128608263}}</ref> The factors that have been used for landslide hazard analysis can usually be grouped into [[geomorphology]], [[geology]], land use/land cover, and [[hydrogeology]]. Since many factors are considered for landslide hazard mapping, [[GIS]] is an appropriate tool because it has functions of collection, storage, manipulation, display, and analysis of large amounts of spatially referenced data which can be handled fast and effectively.<ref>{{cite journal|last1=Clerici|first1=A|last2=Perego|first2=S|last3=Tellini|first3=C|last4=Vescovi|first4=P|title=A procedure for landslide susceptibility zonation by the conditional analysis method1 |journal=Geomorphology |volume=48 |pages=349–364 |year=2002 |doi=10.1016/S0169-555X(02)00079-X |issue=4|bibcode = 2002Geomo..48..349C }}</ref> Cardenas reported evidence on the exhaustive use of GIS in conjunction of uncertainty modelling tools for landslide mapping.<ref>{{cite journal |last=Cardenas |first=IC|title=Landslide susceptibility assessment using Fuzzy Sets, Possibility Theory and Theory of Evidence. Estimación de la susceptibilidad ante deslizamientos: aplicación de conjuntos difusos y las teorías de la posibilidad y de la evidencia. |journal=Ingenieria e Investigación |year=2008 |volume=28 |issue=1 |url=https://www.researchgate.net/publication/232301120}}</ref><ref>{{cite journal|last=Cardenas|first=IC|title=Non-parametric modeling of rainfall in Manizales City (Colombia) using multinomial probability and imprecise probabilities. Modelación no paramétrica de lluvias para la ciudad de Manizales, Colombia: una aplicación de modelos multinomiales de probabilidad y de probabilidades imprecisas|journal=Ingenieria e Investigación |year=2008|volume=28|issue=2|url=https://www.researchgate.net/publication/232301359}}</ref> [[Remote sensing]] techniques are also highly employed for landslide hazard assessment and analysis. Before and after aerial photographs and satellite imagery are used to gather landslide characteristics, like distribution and classification, and factors like slope, [[lithology]], and land use/land cover to be used to help predict future events.<ref>{{cite journal|last1=Metternicht|first1=G|author-link=Graciela Metternicht|last2=Hurni|first2=L|last3=Gogu|first3=R|year=2005|title=Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments|journal=Remote Sensing of Environment|volume=98|issue=2–3|pages=284–303|bibcode=2005RSEnv..98..284M|doi=10.1016/j.rse.2005.08.004}}</ref> Before and after imagery also helps to reveal how the landscape changed after an event, what may have triggered the landslide, and shows the process of regeneration and recovery.<ref>{{cite journal |last1=De La Ville|first1=Noemi|last2=Chumaceiro Diaz|first2=Alejandro|last3=Ramirez|first3=Denisse|url=http://www.vliz.be/imisdocs/publications/35254.pdf |archive-url=https://ghostarchive.org/archive/20221009/http://www.vliz.be/imisdocs/publications/35254.pdf |archive-date=2022-10-09 |url-status=live|title=Remote Sensing and GIS Technologies as Tools to Support Sustainable Management of Areas Devastated by Landslides|journal=Environment, Development and Sustainability|volume=4|pages=221–229|year=2002|doi=10.1023/A:1020835932757|issue=2|bibcode=2002EDSus...4..221D |s2cid=152358230}}</ref>

Using satellite imagery in combination with GIS and on-the-ground studies, it is possible to generate maps of likely occurrences of future landslides.<ref>{{Cite book |title=Advances in remote sensing for infrastructure monitoring |date=2021 |publisher=Springer |isbn=978-3-030-59109-0 |editor-last=Singhroy |editor-first=Vernon |series=Springer remote sensing/photogrammetry |location=Cham}}</ref><ref>{{cite journal|last1=Fabbri|first1=Andrea G.|last2=Chung|first2=Chang-Jo F.|last3=Cendrero|first3=Antonio|last4=Remondo|first4=Juan|title=Is Prediction of Future Landslides Possible with a GIS?|journal=Natural Hazards|volume=30|pages=487–503|year=2003|doi=10.1023/B:NHAZ.0000007282.62071.75|issue=3|bibcode=2003NatHa..30..487F |s2cid=129661820}}</ref> Such maps should show the locations of previous events as well as clearly indicate the probable locations of future events. In general, to predict landslides, one must assume that their occurrence is determined by certain geologic factors, and that future landslides will occur under the same conditions as past events.<ref>{{cite journal|last1=Lee|first1=S|last2=Talib|first2=Jasmi Abdul|title=Probabilistic landslide susceptibility and factor effect analysis|journal=Environmental Geology|volume=47|pages=982–990|year=2005|doi=10.1007/s00254-005-1228-z|issue=7|s2cid=128534998}}</ref> Therefore, it is necessary to establish a relationship between the geomorphologic conditions in which the past events took place and the expected future conditions.<ref>{{cite journal|last1=Ohlmacher|first1=G|title=Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA|journal=Engineering Geology|volume=69|pages=331–343|year=2003|doi=10.1016/S0013-7952(03)00069-3|issue=3–4|bibcode=2003EngGe..69..331O}}</ref>

[[Natural disasters]] are a dramatic example of people living in conflict with the environment. Early predictions and warnings are essential for the reduction of property damage and loss of life. Because landslides occur frequently and can represent some of the most destructive forces on earth, it is imperative to have a good understanding as to what causes them and how people can either help prevent them from occurring or simply avoid them when they do occur. [[Sustainable land management]] and development is also an essential key to reducing the negative impacts felt by landslides.

[[File:SlideMinder Extensometer.png|thumb|A Wireline extensometer monitoring slope displacement and transmitting data remotely via radio or Wi-Fi. In situ or strategically deployed extensometers may be used to provide early warning of a potential landslide.<ref>{{cite journal |last1=Rose |first1=Nick D. |last2=Hunger |first2=Oldrich |url=http://www.engr.usask.ca/classes/GEOE/315/notes/SlopeFailure_Velocity.pdf |title=Forecasting potential slope failure in open pit mines |url-status=dead |archive-url=https://web.archive.org/web/20170713063040/http://www.engr.usask.ca/classes/GEOE/315/notes/SlopeFailure_Velocity.pdf |archive-date=2017-07-13 |journal=Journal of Rock Mechanics & Mining Sciences |date=17 February 2006 |access-date=20 August 2015}}</ref>]]

GIS offers a superior method for landslide analysis because it allows one to capture, store, manipulate, analyze, and display large amounts of data quickly and effectively. Because so many variables are involved, it is important to be able to overlay the many layers of data to develop a full and accurate portrayal of what is taking place on the Earth's surface. Researchers need to know which variables are the most important factors that trigger landslides in any given location. Using GIS, extremely detailed maps can be generated to show past events and likely future events which have the potential to save lives, property, and money.

Since the '90s, GIS have been also successfully used in conjunction to [[decision support system]]s, to show on a map real-time risk evaluations based on monitoring data gathered in the area of the [[Val Pola landslide|Val Pola disaster]] (Italy).<ref>{{Cite journal|last1=Lazzari|first1=M.|last2=Salvaneschi|first2=P.|year=1999|title=Embedding a Geographic Information System in a Decision Support System for Landslide Hazard Monitoring|url=https://dinamico2.unibg.it/lazzari/doc/embedding-authors-copy.pdf |archive-url=https://ghostarchive.org/archive/20221009/https://dinamico2.unibg.it/lazzari/doc/embedding-authors-copy.pdf |archive-date=2022-10-09 |url-status=live|journal=Natural Hazards|volume=20|issue=2–3|pages=185–195|doi=10.1023/A:1008187024768|bibcode=1999NatHa..20..185L |s2cid=1746570}}</ref>

<gallery widths="200px" heights="155px" class="center">
Image:Global Landslide Risks.jpg|Global landslide risks
File:2024 United States landslide map.jpg|2024 United States landslide risk map
File:Ferguson-slide.jpg|[[Ferguson landslide|Ferguson Slide]] on [[California State Route 140]] in June 2006
File:Rock_slide_detector_UPRR_Sierra_grade_at_"Cape_Horn",_Colfax,_CA.jpg|Trackside rock slide detector on the [[Union Pacific Railroad|UPRR]] Sierra grade near [[Colfax, California|Colfax, CA]]
</gallery>