Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

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The main aim of this study is to comprehensively analyze the dynamics of land use
and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical,
current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA)
based on artificial neural network (ANN) concepts was used using cartographic variables such as
environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML
models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study
is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through
time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2
in 2050, signifying better land management or increasing human activity. Vegetative expanses, on
the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance
between urbanization and ecological conservation. Agricultural fields are expected to increase from
2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes
are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water
resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater
reservoir sustainability. These findings are confirmed by strong statistical indices, with this study’s
high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable
level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in
MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of
this study can be used to inform land use policy and plans for sustainable development in the region
and beyond.

Saurabh Kumar
Suraj Kumar