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比较德尔菲模糊层次分析法和模糊逻辑隶属度在土壤肥力评估中的应用:印度孙德尔本斯生物保护区恒河三角洲的一项研究。

Comparing Delphi-fuzzy AHP and fuzzy logic membership in soil fertility assessment: a study of an active Ganga Delta in Sundarban Biosphere Reserve, India.

机构信息

Department of Geography, Presidency University, Kolkata, West Bengal, India.

Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.

出版信息

Environ Sci Pollut Res Int. 2023 Nov;30(55):116688-116714. doi: 10.1007/s11356-022-21983-4. Epub 2022 Jul 29.

Abstract

The present study led to setting up a grid-based soil fertility map along with the best fit model in the coastal regions based on soil physical (coarse, sand, silt, clay, bulk density), chemical (CEC, pH, and soil organic carbon), topographic (elevation), and nutrient elements (PO, KO, Na, Zn, B) in the active Ganga deltaic region of Sundarban Biosphere Reserve, India. Soil samples have been collected from 30 soil grids, and 0-15 cm soil depth was preferred for fertility analysis because most essential soil chemical and nutrient elements affecting soil fertility are concentrated in this depth range. We have used the fuzzy-AHP-Delphi (FAHP) and fuzzy logic-Delphi (FL) methods to determine the soil fertility zone. The rules are generated on the MATLAB interface in the text form; the words "IF," "THEN," "IS," "AND," etc., are used to complete the mode-building process. The weights and the desirable limits for each criterion were set based on the expert opinions and existing literature. The kriging interpolation method and natural break classification were used to represent the soil fertility maps into five classes, namely very high fertility (0.80-1.0), high fertility (0.60-0.80), moderate fertility (0.40-0.60), low fertility (0.20-0.40), and very low fertility (0.00-0.20) respectively. Both the models show that soil fertility is respectively higher near the Hooghly River bank. In many cases, the results obtained from FAHP and FL are quite similar but huge dissimilarity has been noticed in grid numbers G2, G3, G4, F1, and F2. Since the FAHP method has been used for the weight of each criterion, therefore, it only prefers those more important parameters over others. The overall accuracy of the soil fertility map was 82.16% for the fuzzy logic model, and 79.62% for the FAHP model and the kappa coefficient value was determined as 0.82 for the fuzzy logic model and 0.79 for the FAHP model. The soil fertility map was validated using the success rate curve under the ROC technique, and the area under curve (AUC) was calculated as 84.02% for the fuzzy logic model and 81.60% for the FAHP model. Since the standard limits for each criterion were known, therefore, fuzzy logic was found to best fit the model for analyzing soil fertility for each grid.

摘要

本研究在印度孙德尔本斯生物保护区的恒河三角洲活动区,基于土壤物理特性(粗砂、砂、粉砂、粘土、容重)、化学特性(CEC、pH 值和土壤有机碳)、地形(海拔)和养分元素(PO、KO、Na、Zn、B),建立了一个基于网格的土壤肥力图,并确定了最佳拟合模型。从 30 个土壤网格中采集了土壤样本,并且由于影响土壤肥力的大多数基本土壤化学和养分元素都集中在这个深度范围内,因此选择了 0-15cm 的土壤深度进行肥力分析。我们使用模糊层次分析法-德尔菲法(FAHP)和模糊逻辑-德尔菲法(FL)来确定土壤肥力带。规则是在 MATLAB 界面上以文本形式生成的;使用“IF”、“THEN”、“IS”、“AND”等词来完成模式构建过程。权重和每个标准的理想限制是基于专家意见和现有文献确定的。使用克里金插值法和自然断点分类将土壤肥力图表示为五个类别,即极高肥力(0.80-1.0)、高肥力(0.60-0.80)、中肥力(0.40-0.60)、低肥力(0.20-0.40)和极低肥力(0.00-0.20)。两种模型都表明,在 Hooghly 河岸附近的土壤肥力更高。在许多情况下,FAHP 和 FL 得到的结果非常相似,但在网格 G2、G3、G4、F1 和 F2 中注意到了巨大的差异。由于 FAHP 方法用于每个标准的权重,因此它只优先考虑其他参数。模糊逻辑模型的土壤肥力图整体准确性为 82.16%,FAHP 模型为 79.62%,kappa 系数值分别为模糊逻辑模型的 0.82 和 FAHP 模型的 0.79。使用 ROC 技术下的成功率曲线对土壤肥力图进行了验证,模糊逻辑模型的曲线下面积(AUC)为 84.02%,FAHP 模型为 81.60%。由于每个标准的标准限制是已知的,因此发现模糊逻辑最适合分析每个网格的土壤肥力模型。

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