Erratum for “Attribution of Hydrologic Changes in a Tropical River Basin to Rainfall Variability and Land-Use Change: Case Study from India” by S. Setti, R. Maheswaran, D. Radha, V. Sridhar, K. K. Barik, and M. L. Narasimham

Setti, Sridhara; Rathinasamy, Maheswaran; Radha, D.; Sridhar, Venkataramana; Barik, Kamal Kumar; Narasimham, M. L.

doi:10.1061/(asce)he.1943-5584.0002025

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“…Remote sensing is the most commonly used method of mapping land cover and tracking its changes over time (Phan et al, 2020). With an increasing population and the need to develop new areas to meet the demand for food production, energy generation, and water security, the hydrological and water resource modeling community is interested in integrating and evaluating land use changes and their effects on watershed areas (Kang et al, 2019;Setti et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Land Use and Land Cover Analysis Using Google Earth Engine (Case Study: Wanggu Watershed)

Aldiansyah¹,

Saputra

2023

IJReSES

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Human population growth and land use and land cover (LULC) change have always developed side by side. Considering selection of a good Machine Learning (ML) classifier algorithm is needed considering the high estimation of LULC maps based on remote sensing. This study aims to produce a LULC classification of Landsat-8 and Sentinel-2 images by comparing the accuracy performance of three ML algorithms, namely: Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM). Dataset comparison ratios were also explored to find the LULC classification results with the best accuracy. Sentinel-2 is better than Landsat-8 regarding Overall Accuracy (OA) and Coefficient Kappa. The comparison ratio of the training and testing datasets with a good level of accuracy is 70:30 on both images with the average OA Landsat-8 and Sentinel-2 being 92.09% and 94.21%, respectively. The RF algorithm outperforms CART and SVM in both types of satellite imagery. The mean OA of the CART, RF, and SVM classifiers was 92.03%, 94.74%, 83.54% on Landsat-8, 93.14%, 96.15%, and 93.34% on Sentinel-2, respectively.

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Section: Introductionmentioning

confidence: 99%