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The occurrence of fires in tropical peatlands poses significant threats to their ecosystems. An Internet of Things (IoT) system was developed to measure and collect fire risk factors in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, to address this issue. In this paper, neural networks with different layers were employed to predict peatland forests' Fire Weather Index (FWI). The neural network models used two sets of input parameters, consisting of four and nine fire factors. The predicted FWI values were compared with actual values obtained from the Malaysian meteorological department. The findings revealed that the five-layer neural network outperformed others in both the four-input and nine-input models. Specifically, the nine-input neural network achieved a mean square error (MSE) of 1.116 and a correlation of 0.890, surpassing the performance of the four-input neural network with the MSE of 1.537 and the correlation of 0.852. These results hold significant research and practical implications for precise peatland fire prevention, control, and the formulation of preventive measures. [ABSTRACT FROM AUTHOR]

Light rail transit (LRT) systems are vital aspects of the worldwide endeavor to achieve transport sustainability and have been essential in enhancing the economies of urban areas. Issues such as pollution, the risk of road accidents, and traffic congestion could be resolved using this progressive alternative. The statistics showed that only 20% of the commuters in Malaysia use public transport, including LRT, and 80% use private transportation. It is relatively low compared to other Asian countries. High-quality service is essential to improve users' perceived satisfaction with the provided services and increase LRT ridership. The objective of the present study is to acquire an understanding of which factors are crucially influential on users' perceptions of satisfaction. In-person questionnaires were utilized to obtain the information for this paper, with a total of 417 LRT riders in Malaysia's Klang Valley surveyed. This study adopted the factor analysis, correlation test, and artificial neural network (ANN) model. Eight elements related to the quality of service were extracted to ascertain how they influenced the perceived satisfaction of users: information signs, ticket-based services, amenities, safety, employee performance, speed, comfort, and the service details available to riders. Each factor was significantly related to the perceptions of satisfaction, according to the correlation test. Finally, the ANN model shows that the dominant factors determining the LRT users' perceived satisfaction are the signage, amenities, and provision of information. The findings of this research should benefit the providers of services, policy makers, and planning departments by enabling them to formulate successful approaches that ensure user satisfaction is enhanced and the number of riders on the LRT increases. [ABSTRACT FROM AUTHOR]

In many site investigation phases of civil and mining engineering projects, the tensile strength of the rocks is one of the most significant parameters that must be identified. This parameter can be determined directly through laboratory tests. However, conducting such laboratory tests is costly and time consuming. In this paper, a new artificial neural network (ANN)-based model is developed to predict rock tensile strength, using the invasive weed optimization (IWO) technique. Granite samples for the purpose of this research were selected from a tunnel located in Malaysia and underwent appropriate laboratory tests (i.e., Schmidt hammer, point load, dry density, as well as the Brazilian tensile strength (BTS) as system output). A simple regression analysis was carried out, and the obtained results confirmed the need for developing a model with multiple inputs, rather than one with only a single input, in order to predict BTS values. Aiming to highlight the capability of an IWO-ANN model in estimating BTS, artificial bee colony (ABC)-ANN and imperialism competitive algorithm (ICA)-ANN were also applied and developed. The parameters required for the ANN-based models were obtained using different parametric studies. According to calculated performance indices, a new hybrid IWO-ANN model can provide a higher accuracy level for the prediction of BTS compared to the ABC-ANN and ICA-ANN models. The results showed that the IWO-ANN model is a suitable alternative solution for a robust and reliable engineering design. [ABSTRACT FROM AUTHOR]

Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) has been used for a long time and has gained wide attention over the last several years. It can handle a large amount of data and allow non-linear structures by using complex mathematical computations. However, traditional ML models do suffer some problems, such as high bias and overfitting. Therefore, this has resulted in the advancement and improvement of ML techniques, such as the bagging and boosting approach, to address these problems. This study explores a series of ML models to predict the water quality classification (WQC) in the Kelantan River using data from 2005 to 2020. The proposed methodology employed 13 physical and chemical parameters of water quality and 7 ML models that are Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Random Forest and Gradient Boosting. Based on the analysis, the ensemble model of Gradient Boosting with a learning rate of 0.1 exhibited the best prediction performance compared to the other algorithms. It had the highest accuracy (94.90%), sensitivity (80.00%) and f-measure (86.49%), with the lowest classification error. Total Suspended Solid (TSS) was the most significant variable for the Gradient Boosting (GB) model to predict WQC, followed by Ammoniacal Nitrogen (NH3N), Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD). Based on the accurate water quality prediction, the results could help to improve the National Environmental Policy regarding water resources by continuously improving water quality. [ABSTRACT FROM AUTHOR]

Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well-known machine learning techniques and compared their performance to predict the brittleness index of the rock samples. The comparison of the models' performance was conducted through a ranking system. These techniques included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN). This study used a dataset from a water transfer tunneling project in Malaysia. Results of simple rock index tests i.e., Schmidt hammer, p-wave velocity, point load, and density were considered as model inputs. The results of this study indicated that while the RF model had the best performance for training (ranking = 25), the ANN outperformed other models for testing (ranking = 22). However, the KNN model achieved the highest cumulative ranking, which was 37. The KNN model showed desirable stability for both training and testing. However, the results of validation stage indicated that RF model with coefficient of determination (R2) of 0.971 provides higher performance capacity for prediction of the rock BI compared to KNN model with R2 of 0.807 and ANN model with R2 of 0.860. The results of this study suggest a practical use of the machine learning models in solving problems related to rock mechanics specially rock brittleness index. [ABSTRACT FROM AUTHOR]

This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers' robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN's performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency. [ABSTRACT FROM AUTHOR]

The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar reflectivity data to estimate monsoon rainfall using parametric Z-R models. The 10-min reflectivity data recorded in Kota Bahru radar station (in Malaysia) and hourly rain record in nearby 58 gauge stations during 2013–2015 were used. The three-dimensional nearest neighbor interpolation with altitude correction was applied for pixel matching. The non-linear Levenberg Marquardt (LM) regression, integrated with ANN regression minimized the spatiotemporal variability of the proposed Z-R model. Results showed an improvement in the statistical indicator, when LM and ANN overestimated (6.6%) and underestimated (4.4%), respectively, the mean total rainfall. For all rainfall categories, the ANN model has a positive efficiency ratio of >0.2. [ABSTRACT FROM AUTHOR]

An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia. [ABSTRACT FROM AUTHOR]

This study developed a methodological framework to update the rainfall intensity-duration-frequency (IDF) curves under climate change scenarios. A model output statistics (MOS) method is used to downscale the daily rainfall of general circulation models (GCMs), and an artificial neural network (ANN) is employed for the disaggregation of projected daily rainfall to hourly maximum rainfall, which is then used for the development of IDF curves. Finally, the 1st quartiles, medians, and 3rd quartiles of projected rainfall intensities are estimated for developing IDF curves with uncertainty level. Eight GCM simulations under two radiative concentration pathways (RCP) scenarios, namely, RCP 4.5 and RCP 8.5, are used in the proposed framework for the projection of IDF curves with related uncertainties for peninsular Malaysia. The projection of rainfall revealed an increase in the annual average rainfall throughout the present century. The comparison of the projected IDF curves for the period 2006–2099 with that obtained using GCM hindcasts for the based period (1971–2005) revealed an increase in rainfall intensity for shorter durations and a decrease for longer durations. The uncertainty in rainfall intensity for different return periods for shorter duration is found to be 2 to 6 times more compared to longer duration rainfall, which indicates that a large increase in rainfall intensity for short durations projected by GCMs is highly uncertain for peninsular Malaysia. The IDF curves developed in this study can be used for the planning of climate resilient urban water storm water management infrastructure in Peninsular Malaysia. [ABSTRACT FROM AUTHOR]