Monitoring　lateral　displacement　in　deep　excavation　projects　is　crucial　for　structural　stability　and　safety.　Traditional　methods,　like　manual　inclinometers,　are　accurate　but　costly　and　labor-intensive.　Automated　systems　provide　real-time　data　but　face　challenges　with　dense　sensor　placement　and　high　costs.　This　study　presents　a　novel　prediction　method　using　an　extreme　learning　machine　(ELM)　optimized　by　an　improved　particle　swarm　optimization　(IPSO)　algorithm.　The　IPSO-ELM　approach　utilizes　sparse　automated　measurements　to　accurately　predict　lateral　displacement　profiles,　minimizing　the　need　for　dense　sensor　deployment.　A　case　study　of　a　30.2-m-deep　excavation　project　in　Hangzhou,　China,　demonstrates　the　method＇s　effectiveness.　The　results　demonstrate　that　the　IPSO-ELM　model　maintains　high　prediction　accuracy,　with　low　root　mean　square　error　(RMSE)　and　mean　absolute　error　(MAE)　values,　even　under　conditions　of　sparse　sensor　placement.　Across　the　entire　test　dataset,　with　a　sensor　spacing　of　5.0　m,　the　model　achieved　maximum　RMSE　values　ranging　from　0.94　to　2.79　mm　and　maximum　MAE　values　ranging　from　0.77　to　2.18　mm,　thereby　showcasing　its　robustness　and　reliability　in　predicting　lateral　displacement.　A　detailed　discussion　was　conducted　on　the　errors　associated　with　various　sensor　spacing　intervals　when　implementing　the　proposed　method.　This　study　underscores　the　potential　of　IPSO-ELM　as　a　cost-effective　and　reliable　tool　for　automatic　monitoring　in　increasingly　complex　urban　excavation　projects.