Multi‑layer perceptron‑Markov chain‑based artificial neural network for modelling future land‑specific carbon emission pattern and its influences on surface temperature

摘要

Reliable and accurate environmental state prediction can help in long-term sustainable planning and management.Enormous land-use/ land-cover (LULC) transformation has been increasing the carbon emissions (CEs) and land surfacetemperature (LST) around the world. The study aimed to (ⅰ) examine the influences of land specific CEs on LST dynamicsand (ⅱ) simulate future potential LULC, CEs and LST pattern of Khulna City Corporation. Landsat satellite images of theyear 2000, 2010 and 2020 were used to derive LULC, LST and CEs pattern and change. The correlation between land-useindices (NDBI, NDVI, NDWI) and LST was examined to explore the impacts of LULC change on LST. Unplanned urbanizationhas increased 11.79 Km~2(26.10%) buildup areas and 25,268 tons of CEs during 2000–2020. The calculated R~2value indicates the strong positive correlation between Ces and LST. To simulate the future LULC, Ces and LST pattern for theyear 2030 and 2040, multi-layer perceptron-Markov chain (MLP-MC)-based artificial neural network model was utilizedwith the accuracy rate of 94.12%, 99% and 98.48% for LULC, LST and Ces model, respectively. The simulation showsthat by 2040, buildup area will increase to 87.33%, net Ces will increase by 19.82 × 10~4tons, and carbon absorptions willdecrease by 23. 55 × 10~4tons and 69.54% of the total study area’s LST will be above 39°C.Such predictions signify the necessity of implementing a sustainable urban development plan immediately for the sustainable, habitable and sound urban environment.