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Author: search.filters.author.Goyal, A.Subject: search.filters.subject.machine learning

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    Semi physical and machine learning approach for yield estimation of pearl millet crop using SAR and optical data products
    (Mapping Sciences Institute Australia, 2024) Kshetrimayum, A.; Goyal, A.; Ramesh, H.; Bhadra, B.K.
    Pearl millet (Pennisetum glaucum L.R.Br.), is the most widely cultivated food crop after rice, wheat, and maize. The aim of the project is to determine the crop acreage of Pearl millet (Bajra) using Sentinel-1A SAR data and Machine Learning Algorithm to determine the yield estimation of the Pearl millet crop at the tehsil level using the Monteith approach. The classification overall accuracy is found to be 86.48% for Agra district and 80.15% for Firozabad district. The Relative Deviation of yield estimation for the Agra and Firozabad districts is found to be 10.14 and 6, respectively. © 2023 Mapping Sciences Institute, Australia and Geospatial Council of Australia.
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    Exploring different approaches for landslide susceptibility zonation mapping in Manipur: a comparative study of AHP, FR, machine learning, and deep learning models
    (Mapping Sciences Institute Australia, 2024) Kshetrimayum, A.; Ramesh, H.; Goyal, A.
    The movement of rock, soil, and other debris down a slope or incline is a geological phenomenon known as a landslide. To analyze the landslide susceptibility (LS) in Manipur, the study develops and compares six heterogeneous models, specifically the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Deep Learning (DL) models were considered. The study found that the DL is the most intriguing model, with a total accuracy of 97.2%, followed by the RF, KNN, SVM, AHP, and FR, with respective accuracy levels of 94.5%, 93.1%, 92.6%, 85.5%, and 76.9%. © 2024 Mapping Sciences Institute, Australia and Geospatial Council of Australia.