Estimation of the effectiveness of multi-criteria decision analysis and machine learning approaches for agricultural land capability in Gangarampur Subdivision, Eastern India

Abstract

Land suitability analysis (LSA) is an evaluation method that measures the degree to which land is suitable for certain land use. The primary aims of this study are to identify potentially viable agricultural land in the Gangarampur subdivision (West Bengal) using Multiple Criteria Decision Making (MCDM) and machine learning procedures and to evaluate the efficacy of the employed methodologies. The Analytic Hierarchy Process (AHP) model was used to assign relative weights to the fifteen various criteria in this suitability analysis, and then the Fuzzy Complex Proportional Assessment (FCOPRAS) model was applied using the AHP's normalised pairwise comparison matrix, whereas the Waikato Environment for Knowledge Analysis (Weka) Software was used to apply machine learning algorithms to the field data. The Random Forest (RF) model, on the other hand, is a better fit for the locational study of soil potential. According to the RF findings, areas of 14.67 per cent (15368.46 ha) are excellent (ZONE V) for growing crops, approximately 22.30 per cent (23367.9 ha) are highly suitable (ZONE IV), and 23.63 per cent (24762.12 ha) are moderately suitable (ZONE III) for cultivation, respectively. The numbers for FCOPRAS are roughly 15.39% (16130.52 ha), 22.54% (23620.65 ha), and 19.79% (20733.26 ha). The Receiver Operating Characteristic (ROC) curve and accuracy measurements of the results indicate the high accuracy of the applied models, with Random Forest and FCOPRAS being the most popular and effective techniques. This study will make an important contribution to evaluations of soil fertility and site suitability. This will help local government officials, academics, and farmers scientifically use the land.