Support vector machine (SVM) based landform classification in the Precambrian Singhbhum and Rajmahal Protocontinents (Chhotanagpur Plateau), India

Prasanta Mandal; Biswajit Bera

doi:10.1007/s43538-026-00701-5

January 30, 2026Proceedings of the Indian National Science Academy

Support vector machine (SVM) based landform classification in the Precambrian Singhbhum and Rajmahal Protocontinents (Chhotanagpur Plateau), India

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Key Points

The analysis highlights landform classification with a focus on the Precambrian period, indicating geological features.
Utilizing support vector machine, the classification achieved high accuracy rates in distinguishing various landforms across regions.
This research combines geospatial analysis with advanced machine learning techniques to enhance environmental understanding.
The findings may enable more precise geological assessments in ancient continental structures and land formations.

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Implication

Observational analysis reveals landform classification using SVM in the Singhbhum and Rajmahal Protocontinents, indicating advanced geological mapping techniques.

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Mandal et al. (2026) studied this question.

synapsesocial.com/papers/69a75fa0c6e9836116a2b21e https://doi.org/10.1007/s43538-026-00701-5

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Support vector machine (SVM) based landform classification in the Precambrian Singhbhum and Rajmahal Protocontinents (Chhotanagpur Plateau), India

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

Support vector machine (SVM) based landform classification in the Precambrian Singhbhum and Rajmahal Protocontinents (Chhotanagpur Plateau), India

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

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