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Cellular Automata, Hot Mudflow, Probabilistic State Change, LUSI, Prediction, Minimization Difference

A new approach of prediction of Sidoarjo hot mudflow disastered area based on cellular automata with probabilistic adjustment for minimizing prediction errors is proposed. Sidoarjo hot mudflow has specific characteristics such as plane and complex area, huge mud plumes, high viscosity and surface temperature changes, so that it needs combined approaches of slow debris flow, and material changes caused by viscous fluid and thermal changes. Some deterministic approaches can not show the high state changes. This paper presents a new approach of cellular automata using probabilistic state changing to simulate hot mudflow spreading. The model was calibrated with the time series of topological maps. The experimental results show new inundated areas that are identified as high risk areas where are covered by mud. It is also show that the proposed probabilistic cellular automata approach works well for prediction of hot mudflow spreading areas much accurate than the existing conventional methods.