Short-term　prediction　of　water　demand　provides　basic　guarantee　for　water　supply　system　operation　and　management.　In　this　study,　an　effective　model　for　daily　water　demand　forecasting　is　proposed.　Firstly,　principle　component　analysis(PCA)　is　utilized　to　simplify　the　complexity　and　reduce　the　correlation　between　influence　variables,　and　the　score　values　of　selected　principle　components(PCs)　turn　into　the　irrelevant　input　data　of　fuzzy　neural　network(FNN),　which　models　the　prediction　of　water　demand.　Moreover,　an　improved　Levenberg-Marquardt(ILM)　algorithm　is　employed　to　optimize　the　parameters　of　FNN　simultaneously,　the　problems　of　heavy　computing　burden　and　limited　memory　space　can　be　solved.　Most　of　all,　a　growing-pruning　mechanism　based　on　spiking　integrate-and-fire(IF)　model　is　applied　to　FNN　in　order　to　realize　structural　self-organization.　Finally,　contrast　experiments　are　implemented　to　demonstrate　that　the　spiking　self-organizing　fuzzy　neural　network(SSOFNN)　has　better　prediction　performance　and　capability　to　handle　practical　issues.　©　2018,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.