Sudden　water　pollution　accidents　in　surface　waters　occur　with　increasing　frequency.　These　accidents　significantly　threaten　people’s　health　and　lives.　To　prevent　the　diffusion　of　pollutants,　identifying　these　pollution　sources　is　necessary.　The　identification　problem　of　pollution　source,　especially　for　multi-point　source,　is　one　of　the　difficulties　in　the　inverse　problem　area.　This　study　examines　this　issue.　A　new　method　is　designed　by　combining　differential　evolution　algorithm　(DEA)　and　Metropolis–Hastings–Markov　Chain　Monte　Carlo　(MH–MCMC)　based　on　Bayesian　inference　to　identify　multi-point　sudden　water　pollution　sources.　The　effectiveness　and　accuracy　of　this　proposed　method　is　verified　through　outdoor　experiments　and　comparison　between　DEA　and　MH–MCMC.　The　average　absolute　error　of　the　sources’　position　and　intensity,　the　relative　error　and　the　average　standard　deviations　obtained　using　the　proposed　method　are　less　than　those　of　DEA　and　MH–MCMC.　Moreover,　the　relative　error　and　the　sampling　relative　error　under　four　different　standard　deviations　of　measurement　error　(σ = 0.01,　0.05,　0.1,　0.15)　are　less　than　2　and　0.11 %,　respectively.　The　proposed　method　(i.e.,　DEMH–MCMC)　is　effective　even　when　the　standard　deviation　of　the　measurement　error　increases　to　0.15.　Therefore,　the　proposed　method　can　identify　sources　of　multi-point　sudden　water　pollution　accidents　efficiently　and　accurately.　©　2015,　Springer-Verlag　Berlin　Heidelberg.