The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R²) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg⁻¹ for Cr, 20,963.84 ± 4447.83 mg kg⁻¹ for Fe, 1.46 ± 0.42 mg kg⁻¹ for Cd, and 48.71 ± 11.88 mg kg⁻¹ for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination.

Assessing the water quality of surface water bodies is one of the primary duties of environmental authorities in charge of water management. Irrigation water quality (IWQ) of the irrigation canals in the middle Nile delta, Egypt, was assessed by GIS-based research of water suitability indicators (risks connected with salinity, permeability, ion toxicity, and other factors on delicate crops), utilizing a thorough examination of 27 samples gathered from the irrigation canals surrounding the Kitchener drain Egypt, based on thirteen chemical characteristics in 2023. The maps in this work were created with ArcGIS version 10.7. A procedure known as Inverse Distance Weight (IDW) was used to show the variations in the concentrations of the different heavy metals and to offer a geographic representation of the water quality. We utilized principal component analysis (PCA) to pinpoint potential sources of heavy metals. To assess soil contamination levels in the study area, various methods were used such as contamination factors (CFs), heavy metal pollution index (HPI), ecological risks index (ERI), pollution load index (PLI), and the modified degree of contamination (mCd) for seven targeted metals: As, Cd, Co, Cu, Ni, Pb, and Zn. The findings showed that every sample had a medium irrigation appropriateness rating as the IWQI values range from 25.43 to 34.50. According to the different contamination indices, the study area is suffering high contamination as the mean values of HPI, ERI, PLI, and MC_d are 3570.26 ± 621.40, 804.62 ± 164.88, 6.62 ± 6.06, and 5.10 ± 0.89, respectively. PCA results revealed significant metal contamination in multiple enterprises showing that they are present simultaneously and may have a common source. This source could be an industrial discharge, agricultural runoff or other process that affects the metals’ concentrations in surface water. These results give decision-makers important information for managing surface water resources and encouraging sustainable water management in the research region. By educating the local community about artificial groundwater recharge, rainwater collection, and surface water canal management, government authorities can gradually lessen the potential effects of poor water quality in these areas. It is also recommended to develop a risk management module that can assess water threats for agricultural and public health applications. The ultimate goal is to incorporate this descriptive and sensitive research into a risk management system that can generate quick reports for policymakers and decision-makers.

Anthropogenic activities affect the surrounding environment dynamically in different ways. In the arid and hyper arid, agriculture is concentrated in rural communities, which are cooling surfaces that help mitigate surface temperature increases. Recently, rural communities are suffering from increasing urban sprawl. The current work focuses on evaluating the changes in land cover and their impacts on land surface temperature (LST) during (1988–2022) and predicting the changes until 2056 in El-Reyad District, Kafrelsheikh Governorate, Egypt. For achieving this purpose, Landsat images (TM, ETM+, and OLI) were used. The support vector machine (SVM) was applied using Google Earth Engine (GEE) to monitor changes in land use/cover and LST. The prediction of land use until 2056 was achieved using the CA-Markov simulation model. The results showed six land cover classes: agricultural lands, bare lands, urban areas, natural vegetation, Lake Burullus, and fish farms. The results showed the effects of human activity on the conversion of agricultural land to other activities, as agricultural lands have decreased by about 3950.8 acres, while urban areas have expanded by 6283.2 acres, from 1988 to 2022. Fish farms have increased from 3855.6 to 17,612 acres from 1988 to 2022. While the area of bare land decreased from 28.3% to 0.7% of the total area, it was converted to urban, agricultural, and fish farms. The spatiotemporal change in land cover affected the balance of LST in the study area, although the average temperature increased from 32.4 ± 0.5 to 33.6 ± 0.2 °C. In addition, it is expected to reach 36 ± 0.5 °C in 2056, and there are some areas with decreased LST where it is converted from bare areas into fish farms and agricultural uses. The prediction results show that the agricultural area will decrease by −11.38%, the urban area will increase by 4.6%, and the fish farms area will increase by 6.1%. Thus, preserving green spaces and reducing urban sprawl in rural communities are very important objectives.

Site specific is a farming management practice with the potential to address management challenges, this approach leverages precise and timely information on agricultural resources to observe and measure variability in field crops. Site -Specific management zones one of the most important pillars of precision agriculture, this technology is designed to enhance management practices by tailoring agricultural treatments to specific production zones within a field, thereby conserving resources, protecting the environment, and improving crop quality. This paper was aim to developed spatial modeling for Site-Specific Management Zones based on soil and plant parameters. This work was done in Agrofood Company with an area approximately 44.5 Faddan planted with potato crop (DITTA variety) in winter session 2019. The Soil samples were taken before planting the crop and plant samples were taken during the three growth stages based on the grid system, Soil and plant variability maps were produced based on Geostatistical analysis from laboratory analysis of the samples. Spatial Model was developed to delineate site-specific management zones using arcgis 10.8, the input factors to developed this model were Soil maps (N, P, K, Ph, ECe, OM), plant maps (N, P, K, chl-a, chl-b), yield data maps and NDVI to create Site -Specific management zones for the study area. The results showed that Management zones were classified into two Zones, which are zone (I) with an area 26.07 Fadden and its characteristics by high crop yield, good soil parameters, on the other hand zone (II) with an area 18.47 Fadden and its characteristics by low crop yield, low soil and plants parameters

The World Heritage Committee has been meeting to discuss the arrangements of existing World Heritage Sites, and, on 22–26 October, the area from Giza to the Dahshur was included in the list of World Heritage Sites. According to the Egyptian Antiquities Authority (EAA), the groundwater levels at the Pyramids Plateau are too shallow, which threatens the ancient Sphinx and Pyramids in Giza, Egypt. In addition, many geophysical studies have been carried out in the archaeological area of Giza, which prove that the area is facing the risk of a high level of groundwater, specifically threatening the Sphinx. Recent developments in Earth observation have helped in the field of land monitoring such as land use changes, risk observation, and the creation of models for protecting cultural heritage sites. This study aimed to examine the impact of land use changes on on the archaeological sites of the Giza Necropolis area by integrating various data sources including optical satellite imagery and SRTM data during the period of 1965–2019. A historical database of Corona 1965 and Landsat 2009 data was investigated along with the new acquisitions of Sentinel-2 2016 and Sentinel-1 2016 and 2019. In addition, the radar Sentinel-1 SLC data were collected and analyzed for calculating the land subsidence value in the area of interest through two periods between 6–30 July 2016 and 30 July–15 December 2016. Various methods were implemented, including cluster outliers, the Moran index, and spatial autocorrelation to examine the changes in urban masses. Additionally, the relationship between groundwater leakage and land subsidence in the region was investigated. The analysis was carried out using Envi5.3, ArcMap10.6.1, and SNAP6.0 software to extract spatial data from the raw data. The results from our investigation highlighted rapid changes in urban areas between 1965 and 2019. The data obtained and analyzed from optical and radar satellite imagery showed that changes in land use can cause changes in the topographic situation by decreasing the level of groundwater, which adversely affects Egyptian monumental pyramids and the Sphinx. Land use analysis showed that the urban area represented 7.63% of the total area of the study area in 1965, however it reached 32.72% in 2009, approximately half of the total area in 2016, and in 2019, the urban mass area increased to nearly two-thirds of the total area. The annual growth rate between 1965 and 2019 was estimated by nearly 0.642 km²/year. These land-use changes possibly affected the land subsidence value (−0.0138 m), causing the rising groundwater level close to the Sphinx. Using the information obtained from our RS- and GIS-based analysis, mitigation strategies have also been identified to support archaeological area preservation.