In the heart of Nepal, a groundbreaking study is reshaping how we approach urban planning and sustainable development. Milan Budha, a researcher from the Institute of Forestry at Tribhuvan University, has harnessed the power of machine learning and Geographic Information System (GIS) techniques to simulate and predict urban expansion in Chaurjahari Municipality. This research, published in ‘Discover Cities’ (which translates to ‘Explore Cities’), offers a blueprint for sustainable urban growth that could have significant implications for the energy sector and beyond.
Chaurjahari, like many municipalities worldwide, faces the challenge of rapid urbanization. Budha’s study aimed to map, quantify, and predict land use/land cover (LULC) changes, with a particular focus on the expansion of built-up areas. Using Landsat imagery from 2001, 2010, and 2018, Budha classified the data using the maximum likelihood classifier to produce annual LULC maps. The changes were then analyzed using the Land Change Modeler (LCM) in ArcMap 10.5.1, supported by explanatory variables from verified sources to model scenarios for 2030 and 2050.
The results were striking. Built-up areas expanded by 4.28%, primarily replacing agricultural land, which declined by 4.03%. Forest, bare land, and water bodies experienced smaller shifts. The Multi-Layer Perceptron (MLP) model demonstrated a calibration accuracy of 73.4% with a skill measure of 0.6813, while the predicted 2018 map showed an accuracy of 78.48%, validating the model for future projections.
“These findings provide vital insights for the international research community, local governments, and urban planners,” Budha explained. “They support evidence-based planning, sustainable urban growth, and informed policy formulation aligned with global sustainability targets.”
The projections indicate that built-up areas are expected to grow to 9.02% by 2030 and 12.97% by 2050, while agriculture is expected to shrink further. This research is not just about predicting the future; it’s about shaping it. By understanding the dynamics of urban expansion, planners and policymakers can make informed decisions that balance growth with sustainability.
For the energy sector, this research offers a valuable tool for anticipating demand and planning infrastructure. As urban areas expand, so too does the need for reliable energy sources. By predicting where and how cities will grow, energy providers can strategically plan the development of new power plants, distribution networks, and renewable energy projects.
Moreover, the integration of machine learning and GIS techniques in urban planning can lead to more efficient and sustainable resource management. This can reduce costs, minimize environmental impact, and enhance the quality of life for residents. As Budha’s research demonstrates, the future of urban planning is not just about building more; it’s about building smarter.
In the words of Budha, “This study is a step towards creating a more sustainable future. It’s about using technology to make informed decisions that benefit both people and the planet.”
As we look to the future, the insights gained from this research could shape the development of cities around the world. By embracing these technologies and methodologies, we can create urban environments that are not only more livable but also more sustainable, ensuring a brighter future for generations to come.