Wael M. Al-Metwaly

In response to global energy depletion, this study addresses the critical challenge of selecting optimal wind farm sites in Kuwait, a key pillar of the “New Kuwait” 2035 vision for 15% renewable energy integration. A robust, hybrid Multi-Criteria Decision-Making (MCDM) framework was developed, integrating Fuzzy Analytical Hierarchy Process (FAHP) with Entropy-based objective weighting and Type-2 Fuzzy logic within a GIS environment. This approach effectively mitigates expert subjectivity and manages the high “Footprint of Uncertainty” inherent in complex desert environments. Twenty-six technical, environmental, and socioeconomic criteria—including wind power density, proximity to the national grid, and oil field restrictions—were analyzed at a modern utility-scale hub height of 100 m. The spatial results identified approximately 1444 km² (8.6%) of Kuwait’s territory as ' Highly Suitable’ for wind development, primarily concentrated in the western (Al-Jahra) and northern (Al-Abali) corridors. Validation against existing Shagaya Renewable Energy Park and Global Wind Atlas data confirmed a strong spatial correlation (R ² = 0.84). The identified zones represent a potential generation capacity of 2500 MW, providing a data-driven roadmap for national energy planners to meet sustainability targets while minimizing land-use conflicts with oil production and urban expansion.

The lack of precise disease detection systems in date palm farming within the New Valley Governorate hinders timely interventions, leading to increased pesticide use, and significant productivity losses. This research introduces a novel approach integrating remote sensing data processing, GIS techniques, and Normalized Difference Vegetation Index (NDVI) analysis for early and accurate detection of date palm diseases. The proposed cartographic model is designed to map and visualize the spatial distribution of NDVI across date palm fields to identify areas with potential plant stress. Using multispectral and multi-temporal Landsat imagery, we processed near-infrared and red bands. The resulting NDVI data were integrated into a modular environment within GIS software. The model is structured into two main steps: normalization of the near-infrared and red bands and the production of digital cartographic maps visualizing the distribution of NDVI. The results revealed a significant correlation between spatial variations in NDVI and the vertical distribution of date palm areas, with lower NDVI values indicating potential plant health issues. To evaluate the model's reliability, an accuracy assessment analysis was conducted, using metrics such as Kappa coefficient, confirming the robustness and performance of the method. The developed method will facilitate enhanced disease management and contribute to more effective crop health and productivity strategies, as it allows for identification of the locations with possible plant diseases early on, which can help in reducing pesticide usage and improve date palm production. This study is specifically designed to detect common date palm diseases such as [leaf spot diseases targeted].

The increasing number of building and demolition projects results in huge amounts of construction and demolition wastes (CDWs) that are illegally dumped. However, these wastes must be disposed of in appropriate legal sites to protect the environment and human health. After reviewing the literature, no prior research examined optimal site selection for dumping or recycling CDW in an Egyptian city. Furthermore, the absence of field surveys did not offer a holistic understanding of the specific criteria used in the model for this region, nor did it permit an assessment of the suitability of existing dumpsites, thereby revealing certain limitations in the final results. In this regard, this research aims to apply a multi-criteria geographic information system (GIS)-based framework to identify an optimal site for CDW disposal in Kafr El Sheikh City. The criteria affecting the site selection are identified and categorized from prior literature, which are further refined using field surveys and focus group to evaluate their applicability in the context of an Egyptian city. After conducting questionnaire surveys, the trapezoidal interval type II fuzzy analytic hierarchy process is applied to compute the weights of the identified criteria from the perspective of each group of experts. The entropy-based aggregation approach is employed to identify the compromise weights taking into account the preferences of different groups. GIS is a powerful tool for geoprocessing and analyzing spatial big data. The result is a scenario map for the optimal site locations with varying suitability scales (i.e., excellent, very good, good, average, poor, and very poor). The proposed methodology provides what-if scenarios based on a selected set of criteria. According to the results of the multi-criteria decision analysis models, the suitability varies based on the weights of the criteria. For the equal-weighted criteria model, the excellent category covers 5.96% of the study area, increasing to 6.48% for the weighted criteria model. These areas primarily lie in the northeast direction. Conversely, the majority of the study area, 41.80% under equal-weighted criteria and 32.39% under weighted criteria, falls within the average and poor suitability categories, respectively. In general, the most suitable areas are located on the outskirts of the city, and the suitability decreases near the central business district. To bridge the gap between research findings and practical applications, a land use analysis employing satellite imagery is conducted to pinpoint suitable locations for CDW disposal. Existing CDW dumpsites predominantly fall within the range of poor to very good for the equal-weighted criteria model, while the weighted criteria model categorizes them into the poor (16.66%) and average (83.33%) categories. The findings demonstrated the applicability of the proposed framework for CDW disposal management and planning.