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127 results on '"Lawal, Dahiru U."'

38. Advancing SDGs: Predicting Future Shifts in Saudi Arabia's Terrestrial Water Storage Using Multi-Step-Ahead Machine Learning Based on GRACE Data.

Author

Yassin, Mohamed A., Abba, Sani I., Pradipta, Arya, Makkawi, Mohammad H., Shah, Syed Muzzamil Hussain, Usman, Jamilu, Lawal, Dahiru U., Aljundi, Isam H., Ahsan, Amimul, and Sammen, Saad Sh.

Subjects
WATER storage, WATER management, MACHINE learning, STANDARD deviations, WATER use, WATER supply
Abstract

The availability of water is crucial for the growth and sustainability of human development. The effective management of water resources is essential due to their renewable nature and their critical role in ensuring food security and water safety. In this study, the multi-step-ahead modeling approach of the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) was utilized to gain insights into and forecast the fluctuations in water resources within Saudi Arabia. This study was conducted using mascon solutions obtained from the University of Texas Center for Space Research (UT-CSR) over the period of 2007 to 2017. The data were used in the development of artificial intelligence models, namely, an Elman neural network (ENN), a backpropagation neural network (BPNN), and kernel support vector regression (k-SVR). These models were constructed using various input variables, such as t-12, t-24, t-36, t-48, and TWS, with the output variable being the focus. A simple and weighted average ensemble was introduced to improve the accuracy of marginal and weak predictive results. The performance of the models was assessed with the use of several evaluation metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), correlation coefficient (CC), and Nash–Sutcliffe efficiency (NSE). The results of the estimate indicate that k-SVR-M1 (NSE = 0.993, MAE = 0.0346) produced favorable outcomes, whereas ENN-M3 (NSE = 0.6586, MAE = 0.6895) emerged as the second most effective model. The combinations of all other models exhibited accuracies ranging from excellent to marginal, rendering them unreliable for decision-making purposes. Error ensemble methods improved the standalone model and proved merit. The results also serve as an important tool for monitoring changes in global water resources, aiding in drought management, and understanding the Earth's water cycle. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Performance of an internally fan circulated air gap membrane distillation (AGMD) unit.

Author

Lawal, Dahiru U., Mansir, Ibrahim B., Imteyaz, Binash, Abubakar, Abba Abdulhamid, Aliyu, Mansur, and Alawad, Suhaib M.

Subjects
MEMBRANE distillation, HEAT transfer fluids, MASS transfer, TURBULENT mixing, HEAT transfer, WATER temperature, TRADITIONAL farming
Abstract

This work involves an experimental investigation of a novel air gap membrane distillation (AGMD) unit with an installed circulating fan within the condensation chamber. The installed fan breaks the resistance to mass and heat transfer in the gap chamber by inducing fluid mixing and a turbulent dissipation rate, which enhances the overall mass and heat transfer characteristics of the system and consequently the overall performance of the system. The system performance, such as vapor flux, specific thermal energy consumption (STEC), and desalinated water cost, of the new AGMD is evaluated and compared to the traditional AGMD system. The influence of fan design variables, including fan revolution, fan thickness, and fan diameter, on system performance is investigated. Furthermore, the effect of feed water temperature on the system's performance is assessed and presented. Results indicate that fan speed significantly improves the performance of the new AGMD system compared to conventional AGMD designs. Fan thickness recorded a marginal effect, and fan diameter registered some influence, while fan revolution exhibits the strongest impact on the system's performance. The new system can attain a maximum flux and minimum STEC of 34.16 kg/m2·h and 710.72 kWh/m3, respectively, as against the conventional units, which registered a peak vapor flux and lowest STEC of 17.69 kg/m2·h and 1,141.25 kWh/m3, respectively. [ABSTRACT FROM AUTHOR]

Published
2023
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