Abstract
The vegetation of a river basin is affected by various climate factors, such as precipitation and land surface temperature (LST). This study explores the best machine learning model for the prediction of normalized difference vegetation index (NDVI) with LST and precipitation as input parameters. The study also determines the correlation between NDVI, LST, and precipitation of the Mahanadi basin from 2003 to 2021. Monthly precipitation data was extracted from the Center for Hydrometeorology and Remote Sensing (CHRS) portal. The Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive the LST and NDVI using Google Earth Engine (GEE). Four different machine learning models were used to predict the NDVI of the Mahanadi basin: linear regression (LR), random forest (RF), support vector regression (SVR), and k-nearest neighbors (KNN). The coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and explained variance score (EVS) were calculated to evaluate the performance of the models. The results show that the RF model has the highest R2 value in both the training and testing sets among these models, indicating that it is the most optimal among these models for predicting NDVI. The SVR model has the lowest RMSE value in the training set, but the KNN model has the lowest RMSE value in the testing set. The results also show that there is a positive correlation between precipitation and NDVI, a negative correlation between precipitation and LST, and between NDVI and LST. This study provides insights into the relationship between NDVI, LST, and precipitation, and the best machine-learning model for predicting NDVI. The findings of this study can be used to improve the management of river basins and to predict the effects of climate change on vegetation.
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Data availability
The data regarding precipitation for the study area were acquired from the portal of the Center for Hydrometeorology and Remote Sensing portal (https://chrs.web.uci.edu/). NDVI and LST data for the study area were obtained from the USGS website (https://lpdaac.usgs.gov/products/mod13a1v006/) (https://lpdaac.usgs.gov/products/mod11a2v006/).
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The authors extend their sincere gratitude to the editor and anonymous reviewers for their invaluable insights and dedicated efforts in enhancing the quality of our manuscript. Their thoughtful comments and constructive feedback have greatly contributed to its improvement.
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GT conceived the idea, conducted background research, and provided research supervision. DKR contributed to data collection, workflow development, assessment, and led the writing process of the manuscript, with both authors contributing to its composition.
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Raj, D.K., Gopikrishnan, T. Machine learning models for predicting vegetation conditions in Mahanadi River basin. Environ Monit Assess 195, 1401 (2023). https://doi.org/10.1007/s10661-023-12006-x
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DOI: https://doi.org/10.1007/s10661-023-12006-x