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The aim of this research is to assess sustainable green building awareness in Kano State, in a case study of the Gwale local government area. This research makes use of both primary and secondary data to address these offered solutions. Descriptive and quantitative analysis using the BREAM and LEAD evaluation standards was used to analyze the case study and 251 questionnaires were distributed. To ensure a fair trial of each of the 251 building samples, they were chosen at random from various parts of the Gwale Yan-Alawa ward. A case study of a selected green building was chosen and analyzed. The logical comparison with Saudi Arabia was made. It is concluded that the Nigerian government at the national level should put more effort into encouraging green building construction through public awareness programs and incentives and subsidizing the green system. [ABSTRACT FROM AUTHOR]

The degradation of groundwater (GW) quality due to seawater intrusion (SWI) is a major water security issue in water-scarce regions. This study aims to delineate the impact of SWI on the GW quality of a multilayered aquifer system in the eastern coastal region of Saudi Arabia. The physical and chemical properties of the GW were determined via field investigations and laboratory analyses. Irrigation indices (electrical conductivity (EC), potential salinity (PS), sodium adsorption ratio (SAR), Na%, Kelly's ratio (KR), magnesium adsorption ratio (MAR), and permeability index (PI)) and a SWI index (fsea) were obtained to assess the suitability of GW for irrigation. K-mean clustering, correlation analysis, and principal component analysis (PCA) were used to determine the relationship between irrigation hazard indices and the degree of SWI. The tested GW samples were grouped into four clusters (C1, C2, C3, and C4), with average SWI degrees of 15%, 8%, 5%, and 2%, respectively. The results showed that the tested GW was unsuitable for irrigation due to salinity hazards. However, a noticeable increase in sodium and magnesium hazards was also observed. Moreover, increasing the degree of SWI (fsea) was associated with increasing salinity, sodium, and magnesium, with higher values observed in the GW samples in cluster C1, followed by clusters C2, C3, and C4. The correlation analysis and PCA results illustrated that the irrigation indices, including EC, PS, SAR, and MAR, were grouped with the SWI index (fsea), indicating the possibility of using them as primary irrigation indices to reflect the impact of SWI on GW quality in coastal regions. The results of this study will help guide decision-makers toward proper management practices for SWI mitigation and enhancing GW quality for irrigation. [ABSTRACT FROM AUTHOR]

Precise Air temperature modeling is crucial for a sustainable environment. In this study, a novel binary optimized machine learning model, the random vector functional link (RVFL) with the integration of Moth Flame Optimization Algorithm (MFO) and Water Cycle Optimization Algorithm (WCA) is examined to estimate the monthly and daily temperature time series of Rajshahi Climatic station in Bangladesh. Various combinations of temperature and precipitation were used to predict the temperature time series. The prediction ability of the novel binary optimized machine learning model (RVFL-WCAMFO) is compared with the single optimized machine learning models (RVFL-WCA and RVFL-MFO) and the standalone machine learning model (RVFL). Root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2) statistical indexes were utilized to access the prediction ability of the selected models. The proposed binary optimized machine learning model (RVFL-WCAMFO) outperformed the other single optimized and standalone machine learning models in prediction of air temperature time series on both scales, i.e., daily and monthly scale. Cross-validation technique was applied to determine the best testing dataset and it was found that the M3 dataset provided more accurate results for the monthly scale, whereas the M1 dataset outperformed the other two datasets on the daily scale. On the monthly scale, periodicity input was also added to see the effect on prediction accuracy. It was found that periodicity input improved the prediction accuracy of the models. It was also found that precipitation-based inputs did not provided very accurate results in comparison to temperature-based inputs. The outcomes of the study recommend the use of RVFL-WCAMFO in air temperature modeling. [ABSTRACT FROM AUTHOR]

Antiretroviral therapy (ART) is the common hope for HIV/AIDS-treated patients. Total commitments from individuals and the entire community are the major challenges faced during treatment. This study investigated the progress of ART in the Federal Teaching Hospital in Gombe state, Nigeria by using various records of patients receiving treatment in the ART hospital unit. We combined artificial intelligence (AI)-based models and correspondence analysis (CA) techniques to predict and visualize the progress of ART from the beginning to the end. The AI models employed are artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFISs) and support-vector machines (SVMs) and a classical linear regression model of multiple linear regression (MLR). According to the outcome of this study, ANFIS in both training and testing outperformed the remaining models given the R2 (0.903 and 0.904) and MSE (7.961 and 3.751) values, revealing that any increase in the number of years of taking ART medication will provide HIV/AIDS-treated patients with safer and elongated lives. The contingency results for the CA and the chi-square test did an excellent job of capturing and visualizing the patients on medication, which gave similar results in return, revealing there is a significant association between ART drugs and the age group, while the association between ART drugs and marital status (93.7%) explained a higher percentage of variation compared with the remaining variables. [ABSTRACT FROM AUTHOR]

Wastewater treatment and reuse are being regarded as the most effective strategy for combating water scarcity threats. This study examined and reported the applications of the Internet of Things (IoT) and artificial intelligence in the phytoremediation of wastewater using Salvinia molesta plants. Water quality (WQ) indicators (total dissolved solids (TDS), temperature, oxidation-reduction potential (ORP), and turbidity) of the S. molesta treatment system at a retention time of 24 h were measured using an Arduino IoT device. Finally, four machine learning tools (ML) were employed in modeling and evaluating the predicted concentration of the total dissolved solids after treatment (TDSt) of the water samples. Additionally, three nonlinear error ensemble methods were used to enhance the prediction accuracy of the TDSt models. The outcome obtained from the modeling and prediction of the TDSt depicted that the best results were observed at SVM-M1 with 0.9999, 0.0139, 1.0000, and 0.1177 for R2, MSE, R, and RMSE, respectively, at the training stage. While at the validation stage, the R2, MSE, R, and RMSE were recorded as 0.9986, 0.0356, 0.993, and 0.1887, respectively. Furthermore, the error ensemble techniques employed significantly outperformed the single models in terms of mean square error (MSE) and root mean square error (RMSE) for both training and validation, with 0.0014 and 0.0379, respectively. [ABSTRACT FROM AUTHOR]

The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively. [ABSTRACT FROM AUTHOR]

Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. Bagging ensemble (BE), logistic model tree (LT), kernel support vector machine (k-SVM), and k-nearest neighbour (KNN) are the four algorithms used in this study for flood zoning in Jeddah City, Saudi Arabia. The 141 flood locations have been identified in the research area based on the interpretation of aerial photos, historical data, Google Earth, and field surveys. For this purpose, 14 continuous factors and different categorical are identified to examine their effect on flooding in the study area. The dependency analysis (DA) was used to analyse the strength of the predictors. The study comprises two different input variables combination (C1 and C2) based on the features sensitivity selection. The under-the-receiver operating characteristic curve (AUC) and root mean square error (RMSE) were utilised to determine the accuracy of a good forecast. The validation findings showed that BE-C1 performed best in terms of precision, accuracy, AUC, and specificity, as well as the lowest error (RMSE). The performance skills of the overall models proved reliable with a range of AUC (89–97%). The study can also be beneficial in flash flood forecasts and warning activity developed by the Jeddah flood disaster in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Seawater intrusion (SWI) is the main threat to fresh groundwater (GW) resources in coastal regions worldwide. Early identification and delineation of such threats can help decision-makers plan for suitable management measures to protect water resources for coastal communities. This study assesses seawater intrusion (SWI) and GW salinization of the shallow and deep coastal aquifers in the Al-Qatif area, in the eastern region of Saudi Arabia. Field hydrogeological and hydrochemical investigations coupled with laboratory-based hydrochemical and isotopic analyses (18O and 2H) were used in this integrated study. Hydrochemical facies diagrams, ionic ratio diagrams, and spatial distribution maps of GW physical and chemical parameters (EC, TDS, Cl−, Br−), and seawater fraction (fsw) were generated to depict the lateral extent of SWI. Hydrochemical facies diagrams were mainly used for GW salinization source identification. The results show that the shallow GW is of brackish and saline types with EC, TDS, Cl−, Br− concentration, and an increasing fsw trend seaward, indicating more influence of SWI on shallow GW wells located close to the shoreline. On the contrary, deep GW shows low fsw and EC, TDS, Cl−, and Br−, indicating less influence of SWI on GW chemistry. Moreover, the shallow GW is enriched in 18O and 2H isotopes compared with the deep GW, which reveals mixing with recent water. In conclusion, the reduction in GW abstraction in the central part of the study area raised the average GW level by three meters. Therefore, to protect the deep GW from SWI and salinity pollution, it is recommended to implement such management practices in the entire region. In addition, continuous monitoring of deep GW is recommended to provide decision-makers with sufficient data to plan for the protection of coastal freshwater resources. [ABSTRACT FROM AUTHOR]

Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials' contamination with heavy metals (HMs) was conducted. The material's representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil. [ABSTRACT FROM AUTHOR]

Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks. [ABSTRACT FROM AUTHOR]

Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making. [ABSTRACT FROM AUTHOR]

Irrigation systems are becoming increasingly important, owing to the increase in human population, global warming, and food demand. This study aims to design a low-cost autonomous sensor interface to automate the monitoring and control of irrigation systems in remote locations, and to optimize water use for irrigation farming. An internet of things-based irrigation monitoring and control system, employing sensors and actuators, is designed to facilitate the autonomous supply of adequate water from a reservoir to domestic crops in a smart irrigation systems. System development lifecycle and waterfall model design methodologies have been employed in the development paradigm. The Proteus 8.5 design suite, Arduino integrated design environment, and embedded C programming language are commonly used to develop and implement a real working prototype. A pumping mechanism has been used to supply the water required by the soil. The prototype provides power supply, sensing, monitoring and control, and internet connectivity capabilities. Experimental and simulation results demonstrate the flexibility and practical applicability of the proposed system, and are of paramount importance, not only to farmers, but also for the expansion of economic activity. Furthermore, this system reduces the high level of supervision required to supply irrigation water, enabling remote monitoring and control.

We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.