层次分析法
模糊逻辑
土地覆盖
计算机科学
农用地
灵敏度(控制系统)
地理信息系统
环境科学
农业工程
土地利用
环境资源管理
遥感
数学
运筹学
地理
土木工程
人工智能
工程类
电子工程
作者
Swapan Talukdar,Mohd Waseem Naikoo,Javed Mallick,Bushra Praveen,. Shahfahad,Pritee Sharma,Abu Reza Md. Towfiqul Islam,Swades Pal,Atiqur Rahman
标识
DOI:10.1016/j.agsy.2021.103343
摘要
India'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. The present study was conducted to construct India's robust agricultural suitability model by developing hybrid fuzzy logic and the AHP based model. Fourteen 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 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. The 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.
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