Projection of climate variables by general circulation and deep learning model for Lahore,Pakistan |
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| Affiliation: | 1. Faculty of Road and Bridge Engineering, The University of Danang–University of Science and Technology, 550000, Viet Nam;2. Faculty of Graduate School of Urban Innovation, Yokohama National University, Japan;3. Electrical and Electronic Engineering, Phenikaa University, Hanoi 12116, Viet Nam;4. Graduate School of Environment and Information Sciences, Yokohama National University, Japan;5. Faculty of Civil Engineering, Mirpur University of Science and Technology (MUST), (AJK), Pakistan;1. College of New Energy and Environment, Jilin University, Jilin Province, China;2. Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA, USA;3. Retired, United States Geological Survey, Louisiana Water Science Center, Baton Rouge, LA, USA;1. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;2. Ecological Modelling Laboratory, Department of Physical & Environmental Sciences, University of Toronto, Toronto, Ontario M1C 1A4, Canada;1. ENSIAS, Mohammed V University in Rabat, Morocco;2. Alkhwarizmi Department, Mohammed VI Polytechnic University, Benguerir, Morocco;1. Soil and Water Engineering, College of Technology And Engineering, MPUAT, Udaipur 313001, Rajasthan, India;2. Division of Agricultural Engineering, IARI, New Delhi 110012, India;3. Water Technology Centre, ICAR-Indian Agricultural Research Institute (IARI), New Delhi 110012, India;4. Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, India;5. Agricultural Engineering Deptt., Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt;1. Civil Engineering Department, University of Sistan and Baluchestan, Zahedan, Iran;2. Water Security & Sustainable Development Hub, School of Engineering, Newcastle University, Newcastle Upon Tyne, UK;3. School of Engineering, Newcastle University, Newcastle upon Tyne, UK;4. Chair of Engineering Hydrology and Water Management, Technical University of Darmstadt, Darmstadt, Germany |
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| Abstract: | 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 parameters 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. |
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