<![CDATA[The 2018 Palu-Sigi-Donggala earthquakes in Center Celebes have caused significant damage to many residential houses due to varying degrees of soil liquefaction over a very wide extent of urban areas unseen in past destructive earthquakes. While soil liquefaction occurred in Palu and Sigi, thus providing researchers with wide range of characterize soil and site response to large-scale earthquake shaking. One of important learn issues is prediction of liquefaction. Prediction of liquefaction is also a complex problem as it depends on many different physical factors, and the relations between these factors are highly non-linear and complex. Most of these approaches are based on classical statistical approaches and neural networks. In this paper a new approach which is based on classification data mining (DM) is proposed. The proposed approach is based on historical data from the field and sciences portal. The proposed algorithm is also compared with several other DM algorithms based on rminer. It is shown that the proposed algorithm is very effective and accurate in prediction of liquefaction.]]>
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