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

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Coupling geographic information system integrated fuzzy logic-analytical hierarchy process with global and machine learning based sensitivity analysis for agricultural suitability mapping
Agricultural Systems ( IF 6.1 ) Pub Date : 2021-12-08 , DOI: 10.1016/j.agsy.2021.103343
Swapan Talukdar _{1,

2} , Mohd Waseem Naikoo ₂ , Javed Mallick ₃ , Bushra Praveen ₄ , Shahfahad ₂ , Pritee Sharma ₄ , Abu Reza Md. Towfiqul Islam ₅ , Swades Pal ₆ , Atiqur Rahman ₂

Affiliation

CONTEXT

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.

OBJECTIVES

The present study was conducted to construct India's robust agricultural suitability model by developing hybrid fuzzy logic and the AHP based model.

METHODS

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 AND CONCLUSIONS

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.

SIGNIFICANCE

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.

中文翻译：

将模糊逻辑分析层次结构与基于全局和机器学习的敏感性分析相结合的地理信息系统用于农业适宜性制图

语境

印度不断增长的人口增长和不系统的土地覆盖转变导致土地退化和农业生产下降。为了从土地中获得最佳优势，必须适当开发其资源。遥感、高级模糊逻辑和多标准决策，如层次分析法 (AHP) 综合农业土地适宜性分析 (ALAS) 可以促进识别和制定智慧农业所需的有效农业管理策略。

目标

本研究旨在通过开发混合模糊逻辑和基于层次分析法的模型来构建印度的稳健农业适宜性模型。

方法

准备并采用了 14 个地形、气候、土壤相关、土地利用和土地覆盖相关因素来模拟农业适宜性。农业适宜性模型使用三个模糊算子（AND、Gamma 0.8、Gamma 0.9）和混合模糊 AHP 模型预测了基于多参数的全国农业适宜区。使用基于 Moris 技术的全局敏感性分析、随机森林 (RF) 和相关系数进行敏感性分析以测试模型的可靠性。将最佳农业适宜模式与印度主要农作物的生产进行了比较。