Using multivariate statistics and fuzzy logic system to analyse settlement preferences in lowland areas of the temperate zone: an example from the Polish Lowlands

Prediksi Perkembangan Lahan Permukiman Terhadap Kerentanan Bencana Banjir Dan Kebakaran Di Permukiman Tepian Sungai Kapuas Kota Pontianak

“…Faktor penting dalam permukiman adalah jarak dari sumber air. Dalam pola pemukiman, pemodelan diperlukan untuk menilai dampak banjir di permukiman tepian sungai, hal tersebut disebabkan karena perilaku manusia dalam mengelola sumber daya air karena dan kapasitas manajemen sumber air (Anunobi A.I, 2014;Jaroslaw & Hildebrandt-Radke, 2009;Nwilo et al, 2012;Watson & Adams, 2011 Model selular automata umumnya digunakan untuk memprediksi pengembangan lahan yang merupakan proses sejarah tergantung dimana pembangunan itu dilakukan, baik dimasa lalu yang mungkin mempengaruhi masa depan melalui interaksi lokal antara bidang tanah (Wu dan Webster, 1998). Dalam CA simulasi, hasilnya dari iterasi sebelumnya memainkan peran penting pada hasil iterasi berturut-turut.…”

A locally-adaptive model of archaeological potential (LAMAP)

“…A fairly common suite of variables (e.g., Judge and Sebastian, 1988;Wheatley and Gillings, 2002, 166e181;Conolly and Lake, 2006, 179e182;Espa et al, 2006;Jaros1aw and Hildebrand-Radke, 2009;Vaughn and Crawford, 2009;Graves, 2011) was included in the test case for the potential model, which can be categorized into physiographic and resource-oriented types (see Table 1). Particularly with the low-cost and ready availability of medium resolution satellite data, variables based on DEMs should be explored as a matter of course in landscape and settlement studies, and they constitute the basis of the physiographic variables included in this study.…”

“…Predictive models have a long history of use in archaeology (e.g., Green, 1973;King, 1978;Kohler and Parker, 1986;Fry et al, 2004;Finke et al, 2008;Jaros1aw and Hildebrand-Radke, 2009;Crema et al, 2010). Although it is frequently assumed such models predict the probability of encountering undiscovered sites, there is a significant difference between describing known-site locations, predicting the locations of unrecorded archaeological resources on the basis of the knowledge gained by describing known-site locations, and the concept of archaeological potential.…”

GIS-Based and Statistical Approaches in Archaeological Predictive Modelling (NE Romania)

“…Quantitative methods can limit bias, which helps to reduce the effects of this problem and can improve the replicability of the workflow for use in other locations. The most commonly used geospatial formulae for predictive modelling are maximum entropy (MaxEnt) [19], logistic regression [20], evidential reasoning [14], fuzzy logic [21], weight of evidence [22] and multivariate statistics [23].…”