Suspended sediment modeling is an important subject for decision‐makers at the catchment level. Accurate and reliable modeling of suspended sediment load (SSL) is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence‐ (AI‐) based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI‐based models, that is, support vector machine (SVM), adaptive neurofuzzy inference system (ANFIS), feed‐forward neural network (FFNN), and one conventional multilinear regression (MLR) modes, were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble (NNE), ANFIS ensemble (AE), weighted average ensemble (WAE), and simple average ensemble (SAE), were developed by combining the outputs of the four single models to improve their predictive performance. The study used two‐year (2016‐2017) discharge and SSL data for training and verification of the applied models. Determination coefficient (DC) and root mean square error (RMSE) were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI‐based ensemble in particular provide excellent performance in SSL estimation.

The growth of populations and economy in Northern Cyprus has led to continuing utilization of fossil fuels as the primary source of electricity, which will raise environmental pollution. Thus, utilizing renewable energy, particularly solar energy, might be a solution to minimize this issue. This paper presents the potential of grid-connected solar PV power generation at Near East University Hospital (NEU Hospital), one of the largest and leading medical facilities in Northern Cyprus, to meet the energy demand during the daytime to reduce energy bills. For this purpose, the first objective of the study is to evaluate the solar energy potential as a power source for the NEU Hospital based on four datasets (actual measurement, Satellite Application Facility on Climate Monitoring (CMSAF), Surface Radiation Data Set-Heliosat (SARAH), and ERA-5, produced by the European Centre for Medium-range Weather Forecast). The results showed that the solar resource of the selected location is categorized as excellent (class 5), that is, the global solar radiation is within the range of 1843.8–2035.9 kWH/m². The second objective is to investigate the impact of orientation angles on PV output, capacity factor, economic feasibility indicators, and CO₂ emissions by using different PV modules. The results are compared with optimum orientation angles found by Photovoltaic Geographical Information System (PVGIS) simulation software. This objective was achieved by using RETScreen Expert software. The results demonstrated that the highest performance of the proposed system was achieved for orientation angles of 180° (azimuth angle) and −35° (tilt angle). Consequently, it is recommended that orientation angles, PV modules, and market prices are considered to maximize energy production and reduce electricity production costs.

The increased energy demand and related environmental problems caused by burning fossil fuels have raised interest in alternative energy sources. This study investigated the wind characteristics and available wind energy for three urban regions in Northern Cyprus using the Weibull distribution function. The results illustrate that Gazimağusa is the most applicable location for harvesting the kinetic energy of the wind compared to Lefkoşa and Girne. Moreover, the solar potential at a specific location can be analyzed using a different simulation tool. In the present paper, the performance of a rooftop Photovoltaic (PV) system for household buildings in three selected is assessed. Three types of simulation software (PVGIS, PV*SOL, and PVWatts) are used to evaluate the performance of the 6.4 kWp grid-connected rooftop PV system. This study assessed the energy generation, performance ratio and capacity factor for this PV system. The results concluded that PVGIS is an easy, fast, and reliable software tool that can be used for the simulation of a solar PV system in the studied regions. Furthermore, an economic evaluation of renewable systems in the three urban regions is presented. As a result, a small-scale grid-connected solar/wind system that is able to generate electricity with an excellent percentage of clean energy was proposed and developed. The analysis indicates that the proposed PV projects showed significant potential in the studied locations. In addition, the proposed PV system is the most economical option for generating electricity compared to wind systems due to the low electricity prices and of the ability to recover the initial investment. Consequently, it is expected that the simulation results will help in demonstrating the advantages and challenges of installing grid-connected PV systems for households in Northern Cyprus in order to reduce the electricity consumption produced by fossil fuels.