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Predicting land-use change: Intercomparison of different hybrid machine learning models by Landa Sankarrao; Dillip Kumar Ghose; Maheswaran Rathinsamy is a Environmental Science article available to read on EtoBox.

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Land Use Land Cover (LULC) change assessment and prediction are essential for optimised water resources planning and management. This paper attempts to intercompare the different LULC change modelling techniques (two-hybrid and two traditional models) to predict the future LULC change. These include Multi-Layer Perceptron Markov Chain (MLP\_MC), Logistic Regression-Markov Model (LR-MC), and two hybrid models, namely, Multi-Layer Perceptron Markov Chain Cellular Automata (MLP\_MC\_CA), Logistic Regression Markov Model Cellular Automata (LR\_MC\_CA) models. These models were tested on Nagavali River Basin (NRB), a river basin in Southern India, which has seen significant land-use changes over the past two decades. Over the past two decades, the study region experienced dominant changes on the order of 17.42% and 15.22% decrease in scrubland and forest, respectively. At the same time, the agricultural land cover is increased by 35.28%. For the LULC prediction, the model was initially trained using the relevant driver variables and LULC maps of 2010 and 2015. The calibrated model was validated using the 2020 LULC map. The statistical results in terms of Kappa values and chi-square resu

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Author

Landa Sankarrao; Dillip Kumar Ghose; Maheswaran Rathinsamy

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)