Mr. Sarmad, a faculty member of the Civil Engineering Department, recently published a paper titled “Advanced Machine Learning Algorithm to Predict the Implications of Climate…”. His paper focuses on groundwater levels in Selangor, Malaysia, by developing machine learning models. Using 11 months of meteorological data from five towns, researchers tested LSTM, XGBoost, ANN, and SVR algorithms for 1-day, 3-day, and 5-day groundwater level forecasts. XGBoost proved to be the most accurate for 1-day predictions, particularly in Paya Indah Wetland (RMSE: 0.026). The best model was also used to project groundwater levels from 2030 to 2039 using climate data from CMIP5. These findings offer valuable insights for local water resource management and future groundwater level prediction under climate change conditions.  doi.org/10.1016/j.gsd.2024.101152