This　paper　presents　a　flexible　structure　Radial　Basis　Function　(RBF)　neural　network　(FS-RBFNN)　and　its　application　to　water　quality　prediction.　The　FS-RBFNN　can　vary　its　structure　dynamically　in　order　to　maintain　the　prediction　accuracy.　The　hidden　neurons　in　the　RBF　neural　network　can　be　added　or　removed　online　based　on　the　neuron　activity　and　mutual　information　(MI),　to　achieve　the　appropriate　network　complexity　and　maintain　overall　computational　efficiency.　The　convergence　of　the　algorithm　is　analyzed　in　both　the　dynamic　process　phase　and　the　phase　following　the　modification　of　the　structure.　The　proposed　FS-RBFNN　has　been　tested　and　compared　to　other　algorithms　by　applying　it　to　the　problem　of　identifying　a　nonlinear　dynamic　system.　Experimental　results　show　that　the　FS-RBFNN　can　be　used　to　design　an　RBF　structure　which　has　fewer　hidden　neurons:　the　training　time　is　also　much　faster.　The　algorithm　is　applied　for　predicting　water　quality　in　the　wastewater　treatment　process.　The　results　demonstrate　its　effectiveness.　Crown　Copyright　(C)　2011　Published　by　Elsevier　Ltd.　All　rights　reserved.