Abdulrahman Saad Alqahtani

One of the most crucial strategies for allowing us to carry on with normal life both during and after the pandemic is the smart city. The goal of the smart city is to serve the community and enhance the services offered to the populace, particularly in areas as important as health, education, electricity, transportation, and communications. The Internet of Things (IoT) platform offers seamless data transmission and analysis in real time between optical devices and the cloud infrastructure. Cities may enhance several areas of urban life, such as traffic control, video surveillance, environmental monitoring, and infrastructure maintenance, by utilizing the power of optics and IoT. Optical sensors can detect air quality, noise levels, and weather conditions, providing important information for environmental management and public health programs. Intelligent analytics-equipped video surveillance cameras can improve security by spotting and immediately notifying authorities of questionable activity. This research covers two cutting-edge IoT applications for smart cities: The first involves creating an intelligent health monitoring system that aims to lessen work strain on the medical personnel and the spread of Coronavirus infection from patients to the medical staff. The second use deals with measuring and monitoring electrical characteristics and energy consumption to keep track of electrical energy used. Additionally, knowing the trespasser on the electrical line is helpful. Both systems collect data from sensors in real time and send it to a server.

In this study, a near-dry electrical discharge machining (NDEDM) process has been conducted using compressed air mixed with a low quantity of biodegradable refined sunflower oil (called oil-mist) to investigate the machining characteristics. The Box-Behnken method looks at how oil flow rate (OR), air pressure (AR), spark current (SC), and pulse width (PW) affect gas emission concentration (GEC), material removal rate (MRR), and surface roughness (SR). The TOPSIS (The Technique for Order of Preference by Similarity to the Ideal Solution) technique estimates the parameter optimal set for the best machining characteristics. The optimal machining parameters have been used to examine the microstructure of the machined surfaces using a scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDS) analysis. The 0.981 mg/min of GEC, 55.145 mg/min of MRR, and 2.43 µm of surface roughness have been attained by the 14 ml/min flow rate, 7 bar of air pressure, 10 A spark current, and 48 µs pulse duration of the sun-flower oil-mist NDEDM process.

Drought is a severe environmental disaster that results in significant social and economic damage. As such, efficient mitigation plans must rely on precise modeling and forecasting of the phenomenon. This study was designed to enhance drought forecasting through developing and evaluating the applicability of three hybrid models—the hidden Markov model–genetic algorithm (HMM–GA), the auto-regressive integrated moving average–genetic algorithm (ARIMA–GA), and a novel auto-regressive integrated moving average–genetic algorithm–ANN (ARIMA–GA–ANN)—to forecast the standard precipitation index (SPI) in the Bisha Valley, Saudi Arabia. The accuracy of the models was investigated and compared with that of classical HMM and ARIMA based on a performance evaluation and visual inspection. Furthermore, the multi-class Receiver Operating Characteristic-based Area under the Curve (ROC–AUC) was applied to evaluate the ability of the hybrid model to forecast drought events. We used data from 1968 to 2008 to train the models and data from 2009 to 2019 for validation. The performance evaluation results confirmed that the hybrid models provided superior results in forecasting the SPI one month in advance. Furthermore, the results demonstrated that the GA-induced improvement in the HMM forecasts was matched by an approximate 16.40% and 23.46% decrease in the RMSE in the training and testing results, respectively, compared to the classical HMM model. Consequently, the RMSE values of the ARIMA–GA model were reduced by an average of 10.06% and 9.36% for the training and testing processes, respectively. Finally, the ARIMA–GA–ANN, which combined the strengths of the linear stochastic model ARIMA and a non-linear ANN, achieved a greater reduction values in RMSE by an average of 32.82% and 27.47% in comparison with ARIMA in the training and testing phases, respectively. The ROC–AUC results confirmed the capability of the developed models to distinguish between events and non-events with reasonable accuracy, implying the appropriateness of these models as a tool for drought mitigation and warning systems.

The precise prediction of the streamflow of reservoirs is of considerable importance for many activities relating to water resource management, such as reservoir operation and flood and drought control and protection. This study aimed to develop and evaluate the applicability of a hidden Markov model (HMM) and two hybrid models, i.e., the support vector machine-genetic algorithm (SVM-GA) and artificial neural fuzzy inference system-genetic algorithm (ANFIS-GA), for reservoir inflow forecasting at the King Fahd dam, Saudi Arabia. The results obtained by the HMM model were compared with those for the two hybrid models ANFIS-GA and SVM-GA, and with those for individual SVM and ANFIS models based on performance evaluation indicators and visual inspection. The results of the comparison revealed that the ANFIS-GA model and ANFIS model provided superior results for forecasting monthly inflow with satisfactory accuracy in both training (R² = 0.924, 0.857) and testing (R² = 0.842, 0.810) models. The performance evaluation results for the developed models showed that the GA-induced improvement in the ANFIS and SVR forecasts was matched by an approximately 25% decrease in RMSE and around a 13% increase in Nash–Sutcliffe efficiency. The promising accuracy of the proposed models demonstrates their potential for applications in monthly inflow forecasting in the present semiarid region.