Ali Omran Al-Sulttani

Bio: Ali Omran Al-Sulttani is an academic researcher from University of Baghdad. The author has contributed to research in topics: Solar still & Soil contamination. The author has an hindex of 4, co-authored 11 publications receiving 70 citations. Previous affiliations of Ali Omran Al-Sulttani include Universiti Putra Malaysia.

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

Abstract: An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO4), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R2, RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

Abstract: The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is ...

Recent fresh water augmentation techniques in solar still and HDH desalination – A review

Abstract: This article reports about the recent reformation carried out in the solar still and humidification dehumidification desalination system using renewable energy sources for improving the fresh water production rate In solar still, the impact of integration of fins, usage of energy storing materials and wick materials, mixing of nanoparticles, agitation effect, transparent cover cooling, integration of thermoelectric cooler, multi effect of solar still, preheating with water heater and photovoltaic thermal collector, refine the condensing cover, operating with heat pump and refrigeration and integration with waste heat recovery are discussed The recent recast carried out in the packed bed humidification and bubble column desalination using solar energy and biomass energy is also reported It is observed that the solar still water output remarkably enhances with the integration of solar collectors, nanoparticles in water and cover cooling In HDH desalination, bubble column humidifier found to be better compared to the packed bed and other type of humidifiers Also it is noticed that the biomass based desalination systems are more suitable for the places with lower solar radiation and places with higher biomass sources

Review on applications of particle swarm optimization in solar energy systems

Abstract: Solar energy is one of the most important factors used in the development of the countries. Since it is a renewable energy source, it reduces the demand on the non-renewable energy sources such as fossil fuels, oil, natural gas, nuclear, and other sources. Therefore, many researchers have sought to improve the performance of solar energy systems via applying several metaheuristic methods such as particle swarm optimization (PSO) which simulates the behavior of the fish schools or bird flocks. PSO has been used in different applications including engineering, manufacturing, and medicine. The main process of the PSO is to determine the optimal position for each particle inside the population. This is performed through updating the position using the velocity of each particle and the shared information between the particles. The aim of this paper is to provide a review on the PSO’s applications to improve the performance of solar energy systems and to identify the research gap for future work. The literature review used in this study indicates that the PSO is a very promising method to enhance the performance of solar energy systems.