Short-term　water　demand　forecasting　plays　an　important　role　in　smart　management　and　real-time　simulation　of　water　distribution　systems　(WDSs).　This　paper　proposes　a　hybrid　model　for　the　short-term　forecasting　in　the　horizon　of　one　day　with　15　min　time　steps,　which　improves　the　forecasting　accuracy　by　adding　an　error　correction　module　to　the　initial　forecasting　model.　The　initial　forecasting　model　is　firstly　established　based　on　the　least　square　support　vector　machine　(LSSVM),　the　errors　time　series　obtained　by　comparing　the　observed　values　and　the　initial　forecasted　values　is　next　transformed　into　chaotic　time　series,　and　then　the　error　correction　model　is　established　by　the　LSSVM　method　to　forecast　errors　at　the　next　time　step.　The　hybrid　model　is　tested　on　three　real-world　district　metering　areas　(DMAs)　in　Beijing,　China,　with　different　demand　patterns.　The　results　show　that,　with　the　help　of　the　error　correction　module,　the　hybrid　model　reduced　the　mean　absolute　percentage　error　(MAPE)　of　forecasted　demand　from　(5.64%,　4.06%,　5.84%)　to　(4.84%,　3.15%,　3.47%)　for　the　three　DMAs,　compared　with　using　LSSVM　without　error　correction.　Therefore,　the　proposed　hybrid　model　provides　a　better　solution　for　short-term　water　demand　forecasting　on　the　tested　cases.