5, November 2026

AI-Based Land Suitability Analysis and Recommendation System Using Machine Learning and GIS

VOLUME-12; ISSUE-11; NOV-2026

Author(s): 1.R.M.VAMSIKRISHNA, 2.A.SUSHWANTH, 3.A.GIRIVARDHAN, 4.G.UDAY, 5.MS.RICHATIWARI

Authors Affiliations:

  1. B.Tech Student, Department of Computer Science and Engineering – Data Science, Hyderabad Institute of Technology and Management (HITAM), Hyderabad, Telangana, India.

  2. B.Tech Student, Department of Computer Science and Engineering – Data Science, Hyderabad Institute of Technology and Management (HITAM), Hyderabad, Telangana, India.
  3. B.Tech Student, Department of Computer Science and Engineering – Data Science, Hyderabad Institute of Technology and Management (HITAM), Hyderabad, Telangana, India.
  4. B.Tech Student, Department of Computer Science and Engineering – Data Science, Hyderabad Institute of Technology and Management (HITAM), Hyderabad, Telangana, India.

  5. Assistant Professor, Department of Computer Science and Engineering – Data Science, Hyderabad Institute of Technology and Management (HITAM), Hyderabad, Telangana, India.

DOIs:10.2015/IJIRMF/202611002     |     Paper ID: IJIRMF202611002


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Abstract - Sustainable land-use planning relies on land suitability assessment, because individual parcels differ in how well they serve agricultural, residential, commercial, industrial, or mixed-use purposes. Conventional assessment methods rest largely on manual analysis and expert judgement, which makes them slow and hard to extend to large spatial datasets. This paper describes an AI-Based Land Suitability Analysis and Recommendation System that couples machine learning with Geographic Information System (GIS) techniques to assess land for several purposes. The system examines spatial, environmental, terrain, soil, water, infrastructure, and risk-related factors and produces suitability scores for five land-use categories: agriculture, residential, commercial, industrial, and mixed-use. A machine-learning pipeline predicts these scores and returns a recommended land-use category together with a confidence indicator. A nearby-land analysis adds spatial context for the chosen parcel, and SHAP (SHapley Additive exPlanations) highlights the features that raise or lower each prediction. Through an interactive web interface, users can analyze a parcel by its Land ID and inspect it on a GIS map. Several machine-learning models are compared experimentally using MAE, RMSE, R², accuracy, precision, recall, and F1-score. By uniting predictive modeling, spatial analysis, recommendation, and explainability on one platform, the framework offers an integrated decision-support approach to land assessment.

Keywords - Land Suitability Analysis, Machine Learning, Geographic Information System (GIS), Spatial Analysis, Land-Use Recommendation, Explainable AI, SHAP, Spatial Prediction, Decision Support System)

R.M.VAMSIKRISHNA, A.SUSHWANTH, A.GIRIVARDHAN, G.UDAY, MS.RICHATIWARI (2026); AI-Based Land Suitability Analysis and Recommendation System Using Machine Learning and GIS, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-12, Issue-11, Available on –   https://www.ijirmf.com/

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