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Developing countries all over the world face numerous difficulties with regard to food security. The purpose of this research is to develop a new approach for evaluating wheat's suitability for cultivation. To this end, geographical information systems (GIS) and fuzzy inference systems (FIS) are used as the most appropriate artificial intelligence (AI) tools. Outcomes of investigations carried out in the western Nile Delta, Egypt. The fuzzy inference system used was Mamdani type. The membership functions used in this work are sigmoidal, Gaussian, and zmf membership. The inputs in this research are chemical, physical, and fertility soil indices. To predict the final soil suitability using FIS, it is required to implement 81 IF-THEN rules that were written by some experts. The obtained results show the effectiveness of FIS in predicting the wheat crop's suitability compared to conventional methods. The research region is split into four classes: around 241.3 km2 is highly suitable for wheat growth, and 224 km2 is defined as having moderate suitability. The third soil suitability class (low), which comprises 252.73 km2, is larger than the unsuitable class, which comprises 40 km2. The method given here can be easily applied again in an arid region. Decision-makers may benefit from the research's quantitative findings. [ABSTRACT FROM AUTHOR]

This work is a novel trial to integrate geostatistics with fuzzy logic under the geographic information system (GIS) environment to model soil pollution. Soil samples from seventy-one soil profiles in the northern Nile Delta, Egypt, and were analyzed for total concentrations of Cd, Co, Cu, Pb, Ni, and Zn. Metal distribution maps were generated using ordinary kriging methods. They were normalized by linear and non-linear fuzzy membership functions (FMFs) and overlain by fuzzy operators (And, OR, Sum, Product, and Gamma). The final maps were validated using the area under the curve (AUC) of the receiver operating characteristic (ROC). The best-fitted semivariogram models were Gaussian for Cd, Pb, and Ni, circular for Co and Zn, and exponential for Cu. The ROC and AUC analysis revealed that the non-linear FMFs were more effective than the linear functions for modeling soil pollution. Overall, the highest AUC value (0.866; very good accuracy) resulted from applying the fuzzy Sum overly to the non-linearly normalized layers, implying the superiority of this model for decision-making in the studied area. Accordingly, 92% of the investigated soils were severely polluted. Our study would increase insight into soil metal pollution on a regional scale, especially in arid regions. [ABSTRACT FROM AUTHOR]

One of the most significant challenges that global decision-makers are concerned about is soil contamination. It is also related to food security and soil fertility. The quality of the soil and crops in Egypt are being severely impacted by the increased heavy metal content of the soils in the middle Nile Delta. In Egypt's middle Nile Delta, fifty random soil samples were chosen. Inverse distance weighting (IDW) was used to create the spatial pattern maps for four heavy metals: Cd, Mn, Pb, and Zn. The soil contamination levels in the research area were assessed using principal component analysis (PCA), contamination factors (CF), the geoaccumulation index (I-Geo), and the improved Nemerow pollution index (In). The findings demonstrated that using PCA, the soil heavy metal concentrations were divided into two clusters. Moreover, the majority of the study region (44.47%) was assessed to be heavily to extremely polluted by heavy metals. In conclusion, integrating the contamination indices CF, I-Geo, and In with the GIS technique and multivariate model, analysis establishes a practical and helpful strategy for assessing the hazard of heavy metal contamination. The findings could serve as a basis for decision-makers to create effective heavy metal mitigation efforts. [ABSTRACT FROM AUTHOR]