As　a　key　parameter　in　the　adsorption　process,　removal　rate　is　not　available　under　most　operating　conditions　due　to　the　time　and　cost　of　experimental　testing.　To　address　this　issue,　evaluation　of　the　efficiency　of　NH4+　removal　from　stormwater　by　coal-based　granular　activated　carbon　(CB-GAC),　a　novel　approach,　the　response　surface　methodology　(RSM),　back-propagation　artificial　neural　network　(BP-ANN)　coupled　with　genetic　algorithm　(GA),　has　been　applied　in　this　research.　The　sorption　process　was　modeled　based　on　Box-Behnben　design　(BBD)　RSM　method　for　independent　variables:　Contact　time,　initial　concentration,　temperature,　and　pH;　suggesting　a　quadratic　polynomial　model　with　p-value　2　=　0.9762.　The　BP-ANN　with　a　structure　of　4-8-1　gave　the　best　performance.　Compared　with　the　BBD-RSM　model,　the　BP-ANN　model　indicated　better　prediction　of　the　response　with　R2　=　0.9959.　The　weights　derived　from　BP-ANN　was　further　analyzed　by　Garson　equation,　and　the　results　showed　that　the　order　of　the　variables’　effectiveness　is　as　follow:　Contact　time　(31.23%)　＞　pH　(24.68%)　＞　temperature　(22.93%)　＞　initial　concentration　(21.16%).　The　process　parameters　were　optimized　via　RSM　optimization　tools　and　GA.　The　results　of　validation　experiments　showed　that　the　optimization　results　of　GA-ANN　are　more　accurate　than　BBD-RSM,　with　contact　time　=　899.41　min,　initial　concentration　=　17.35　mg/L,　temperature　=　15　°C,　pH　=　6.98,　NH4+　removal　rate　=　63.74%,　and　relative　error　=　0.87%.　Furthermore,　the　CB-GAC　has　been　characterized　by　Scanning　electron　microscopy　(SEM),　X-ray　diffraction　(XRD)　and　Brunauer-Emmett-Teller　(BET).　The　isotherm　and　kinetic　studies　of　the　adsorption　process　illustrated　that　adsorption　of　NH4+　onto　CB-GAC　corresponded　Langmuir　isotherm　and　pseudo-second-order　kinetic　models.　The　calculated　maximum　adsorption　capacity　was　0.2821　mg/g.　©　2021　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.