Projection of climate variables by general circulation and deep learning model for Lahore, Pakistan

Climate change significantly impacts the hydrological cycle and environment. The key parameters driving climate change for densely populated city of Lahore in Pakistan were studied. The projections of these param-eters were evaluated using General Circulation Model (GCM) named Community Climate System Model (CCSM4) under two Representative Concentration Pathways (RCP 4.5 and 8.5) scenarios. The outputs of CCSM4 model were bias corrected using quantile mapping using historical data. Additionally, a deep learning model named long short-term memory (LSTM) was developed applying machine learning applications to forecast the climate parameters for the future. LSTM model with two LSTM layers including one fully connected layer was modeled for the projection of climate variables in the region. Total number of parameters were 9888, and the input and forecasted output length was kept as 24 sequential months without overlapping. The conventional projection methods of GCM were compared with LSTM outputs for bridging the gap. The LSTM model was found to be more effective and dependable in forecasting the climate with significant improvement in the statistical parameters for the region. The LSTM model can be applied for projections of climate in comparison to GCM with sufficient precision.

Automated summary

Climate change impacts hydrological cycle and environment. This study evaluates the key climate parameters in Lahore, Pakistan using GCM and LSTM models, and finds that LSTM model outperforms GCM in climate forecasting with significant improvement in statistical parameters.