Al-Iraqia Journal for Scientific Engineering Research

Synchronising the Hydraulics Model Builder and NDWI Indexes in Remote Sensing Techniques to estimate accurate Flood Hazard from Satellite Images

2026-09-04T13:48:06+00:00

Water resources are an important natural resource that sustains life and supports the growth of society and an economy. However, it remains an unusual resource that is not always available at the same time or in the same place. Climate change and global warming have worsened extreme weather events, such as heavy rain and flooding, in many parts of the world over the last few decades. This study aims to identify and map regions in Al-Shirqat at risk of flooding through analysis of Landsat 8 OLI data. A Hydraulics Model (HDM) was a distinct structural modelling tool within the ArcGIS environment that utilised the Python programming language to compute water depths from Digital Elevation Model (DEM) data. This suggested method improves standard analytical methods, which involve analysing images for the Normalised Difference Water Index (NDWI). This study’s findings offer a pragmatic approach for enhancing flood risk evaluation and water resource management in analogous geographical contexts.

An Efficient Renewable Power Prediction Model Using IGOA-based Enhanced BiLSTM Deep Neural Network for Microgrid Smart Energy Management

2026-09-04T14:07:03+00:00

Accurate forecasting of renewable energy generation and operational variables is a critical requirement for effective smart energy management in microgrid systems. This paper presents an integrated forecasting framework based on a Bidirectional Long Short-Term Memory network optimized using an Improved Grasshopper Optimization Algorithm (IGOA-BiLSTM). The proposed method exploits the bidirectional learning capability of BiLSTM to model complex nonlinear temporal dependencies, while an enhanced Grasshopper Optimization Algorithm is introduced to optimally tune the network’s hidden layer size. The IGOA incorporates a dynamic inertia weight updating mechanism and an adaptive triangular mutation strategy to improve exploration–exploitation balance, enhance convergence stability, and avoid premature convergence. The model is evaluated using real-world hourly data from the PJM West Zone, including photovoltaic power, wind turbine power, load demand, and day-ahead electricity prices. Experimental results demonstrate that the proposed IGOA-BiLSTM achieves superior forecasting accuracy compared with conventional LSTM and state-of-the-art hybrid models. Specifically, the proposed method attains coefficients of determination (R²) of 0.99 for photovoltaic power, wind power, load demand, and day-ahead price forecasting. For photovoltaic and wind power prediction, RMSE values of 0.019 and 0.015 are achieved, respectively, with corresponding MSE values as low as 0.0003 and 0.0002. In load and day-ahead price forecasting, the model achieves RMSE values of 0.019 and 0.009, respectively. These results confirm the robustness, high accuracy, and computational efficiency of the proposed framework, making it well suited for practical microgrid energy management, renewable energy planning, and electricity market decision-support applications.

Performance Evaluation of Z-Source Inverter Under Different Conditions

2026-09-04T14:31:30+00:00

A suggested PI-controlled closed-loop Z-source inverter (ZSI) constructed in MATLAB/Simulink is presented in this research and evaluated under various conditions. The ZSI topology is suitable for renewable energy systems because it provides single-stage buck-boost operation through shoot-through, unlike traditional inverters such as VSI and CSI, which require additional converter stages. This paper investigated the impact of switching frequency, modulation factor, and load resistance on output voltage and total harmonic distortion (THD). Simulation results show that the output voltage remains constant at 209 V while THD decreases from 0.47% to 0.17% when switching frequency increases from 2 kHz to 10 kHz. Furthermore, the output voltage reaches 209.6 V, and THD remains low at 0.23% at 20 kHz. Simulation also shows that increasing the modulation index from 0.7 to 0.9 improves the waveform. It was also observed that the output voltage increases across loads from 10 Ω to 100 Ω. The controller maintains a low THD of 0.15% to 0.23%. These results confirm that the proposed PI-controlled closed-loop ZSI provides stable operation and good power quality for renewable-energy applications and distributed-generation systems.

EEG-Based Detection of Alzheimer's Disease: A Bibliometric Analysis

2026-09-04T14:41:47+00:00

Alzheimer’s disease (AD) is a progressive brain disorder commonly seen in older adults. It leads to memory loss, cognitive decline, and eventually affects a person's ability to perform everyday tasks. Over time, it can become life-threatening, and its prevalence has been rising significantly. One widely used approach for detecting AD is through electroencephalography (EEG), which measures electrical activity in the brain. This paper contains an overview of detecting Alzheimer’s disease (AD) with EEG in a bibliometric analysis on the publication statistics provided from the Web of Science (WoS) database. This analysis includes basic statistical inferences on the publication counts, yearly distributions, citation performances, distribution on indexing, publication title, countries, etc., in a comparative manner to outline how EEG-based diagnosis differs from the general AD detection studies.

Thermodynamic Assessment of an Integrated PDSC–TES–AMTEC–TEG Solar Micro-CHP System

2026-09-04T14:56:03+00:00

Decentralized renewable energy systems that generate electricity as well as useful heat have been gaining more attention as efficient alternatives to traditional fossil-fuel energy systems. This study reports a thermodynamic analysis of a solid-state solar micro-combined heat and power (mCHP) system that uses an alkali metal thermal-to-electric converter, latent thermal energy storage, a thermoelectric generator, and a parabolic dish solar concentrator. The configuration is designed to optimize solar energy use, integrating high-temperature solar heat collection, thermal storage, direct thermal-to-electric conversion, and waste heat recovery into a compact and modular system. The performance of the integrated system was analyzed using the methods of energy and exergy analysis, and the main sources of irreversibilities were identified. Suitable operating conditions were investigated by studying the effects of the key operating parameters such as receiver wall temperature, AMTEC current, condenser temperature, and TEG current. The results indicate that the proposed system can produce a total electrical power of 7372 W, including 6241 W from the AMTEC and 1131 W from the TEG, as well as useful heating power of 8242 W at 80 °C. The efficiencies for electrical and overall CHP are 24.75% and 52.05%, respectively. Moreover, the electrical exergy efficiency and overall exergy efficiency are 26.4% and 31.01%, respectively, and the overall rate of exergy destruction is 19231 W. From the exergy analysis it is seen that the solar collection stage is the largest source of irreversibility. The proposed PDSC–TES–AMTEC–TEG system has good potential for use as a dependable, modular, and fully solid-state solar micro-CHP system for decentralized energy applications.