The　prediction　models　are　the　basis　for　scientific　formulation　of　emergency　disposal　and　rescue　measures.　In　order　to　quickly　and　accurately　construct　the　forecasting　model　of　sudden　water　pollution　accidents,　this　paper　regards　the　problem　as　the　Bayesian　estimation　problem　and　obtains　the　posterior　probability　density　function　of　the　model　parameters　according　to　the　finite　difference　method　and　Bayesian　inference.　Then,　by　using　the　improved　Metropolis-Hastings　sampling　method,　more　reasonable　prediction　model　parameters　are　obtained.　Taking　the　sudden　water　pollution　event　in　a　certain　open　channel　as　an　example,　the　effects　of　different　observation　noises　on　the　parameters　calibration　results　are　discussed　under　the　two　scenarios　of　non-uniform　flow　control　with　non-uniform　flow　and　non-equal　capacity　control,　and　compared　with　the　parameter　and　true　value　of　the　Bayesian-Markov　chain　Monte　Carlo　method.　The　results　show　that,　the　improved　Bayesian-Markov　chain　Monte　Carlo　method　can　give　a　better　parameter　value,　more　application　and　has　a　good　anti-noise,　which　can　provide　a　new　approach　to　build　the　forecast　model　of　sudden　water　pollution　accidents.　©　2018,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.