As　a　key　water　quality　parameter　in　the　wastewater　treatment　process　(WWTP),　the　accurate　measurement　of　total　phosphorus　(TP)　would　effectively　prevent　the　effluent　water　from　eutrophication.　Although　soft　measurement　models　can　successfully　predict　effluent　TP,　the　model　prediction　is　unreliable　because　outliers　will　inevitably　exist　in　actual　WWTP　due　to　a　variety　of　disturbances.　To　solve　this　problem,　a　novel　robust　small-world　feedforward　neural　network　(RSWFNN)　is　proposed　to　improve　the　robustness　of　effluent　TP　prediction.　First,　the　robust　Spearman　rank　correlation　analysis　is　used　to　determine　auxiliary　variables　intrinsically　correlated　with　the　effluent　TP.　Second,　inspired　by　the　fault　tolerance　of　the　human　brain　from　its　small　world　property,　the　small-worldness　is　introduced　to　obtain　a　robust　network　architecture.　Third,　the　robust　learning　algorithm　using　the　loss　function　of　regularized　M-estimation　is　proposed　to　suppress　the　responses　of　outliers　to　improve　the　robustness　of　the　model.　Finally,　the　corresponding　two　hyperparameters　are　determined　by　an　adaptive　adjustment　strategy,　thus　ensuring　the　effectiveness　of　suppressing　outliers.　Our　experimental　results　have　shown　that　RSWFNN　has　stronger　robustness　and　better　prediction　performance　to　predict　effluent　TP　than　other　modeling　methods,　and　the　superiority　of　robustness　becomes　more　obvious　with　the　increase　of　outlier　proportion.