Coupling geographic information system integrated fuzzy logic-analytical hierarchy process with global and machine learning based sensitivity analysis for agricultural suitability mapping

Abstract

CONTEXTIndia's increasing population growth and unsystematic land cover transformation have led to land degradation and a decline in agricultural production. To achieve optimum advantage from the land, proper exploitation of its resources is necessary. Remote sensing, advanced fuzzy logic, and multi-criteria decision-making like analytical hierarchy process (AHP) integrated agricultural land suitability analysis (ALAS) may facilitate identifying and formulating effective agricultural management strategies required for smart agriculture. OBJECTIVESThe present study was conducted to construct India's robust agricultural suitability model by developing hybrid fuzzy logic and the AHP based model. METHODSFourteen topographical, climatological, soil-related, land-use, and land-cover-related factors were prepared and employed to model agricultural suitability. Agricultural suitability models predicted multi-parameters based agricultural suitable zones for the entire country using three fuzzy operators (AND, Gamma 0.8, Gamma 0.9) and a hybrid fuzzy-AHP model. Sensitivity analysis was conducted to test the models' reliability using Moris technique-based global sensitivity analysis, random forest (RF), and correlation coefficient. The best agricultural suitable model was compared with the production of major crops in India. RESULTS AND CONCLUSIONSResults showed that 19.8% of the study area was permanently not suitable in the northernmost region, 19.7% was currently not suitable in the northernmost region, while 20.1% and 20.2% areas were predicted as moderately suitable and highly suitable zones, respectively. The rainfall, elevation, slopes, evapotranspiration, and aridity index had a prime influence on the output of the agricultural suitability model. SIGNIFICANCEThe adopted method and its application processes can analyze agricultural land suitability and recommend optimal farming methods. It is also comprehended as a promising option for meeting food, nutrition, energy, and job demands while still protecting our threatened environment.