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Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States’ National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting.

Modeling wind speed has a significant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine learning approaches – Gaussian process regression (GPR), bagged regression trees (BTs) and support vector regression (SVR) – were applied for prediction of the weekly wind speed (maximum, mean, minimum) of the target station using other stations, which were specified as reference stations. Daily wind speed data, gathered via the Malaysian Meteorological Department at 14 measuring stations in Malaysia covering the period between 2000 and 2019, were used. The results showed that the average weekly wind speed had superior performance to the maximum and minimum wind speed prediction. In general, the GPR model could effectively predict the weekly wind speed of the target station using the measured data of other stations. Errors found in this model were within acceptable limits. The findings of this model were compared with the measured data, and only Kota Kinabalu station showed an unacceptable range of prediction. To investigate the prediction performance of the proposed model, two models were used as the comparison models: the BTs model and SVR model. Although the comparison of GPR with the BTs model at Kuching station showed slightly better performance for the BTs model in maximum and minimum wind speed prediction, the prediction outcomes of the other 13 stations showed better performance for the proposed GPR model. Moreover, the proposed model generated smaller prediction errors than the SVR model at all stations.

ABSTRACTThe suspended sediment load (SSL) is one of the major hydrological processes affecting the sustainability of river planning and management. Moreover, sediments have a significant impact on dam operation and reservoir capacity. To this end, reliable and applicable models are required to compute and classify the SSL in rivers. The application of machine learning models has become common to solve complex problems such as SSL modeling. The present research investigated the ability of several models to classify the SSL data. This investigation aims to explore a new version of machine learning classifiers for SSL classification at Johor River, Malaysia. Extreme gradient boosting, random forest, support vector machine, multi-layer perceptron and k-nearest neighbors classifiers have been used to classify the SSL data. The sediment values are divided into multiple discrete ranges, where each range can be considered as one category or class. This study illustrates two different scenarios related to the number of categories, which are five and 10 categories, with two time scales, daily and weekly. The performance of the proposed models was evaluated by several statistical indicators. Overall, the proposed models achieved excellent classification of the SSL data under various scenarios.

ABSTRACTA reliable model to predict the changes in the water levels in a river is crucial for better planning to mitigate any risk associated with flooding. In this study, six different Machine Learning (ML) algorithms were developed to predict the river’s water level, on a daily basis based on collected data from 1990 to 2019 which were used to train and test the proposed models. Different input combinations were explored to improve the accuracy of the model. Statistical indicators were calculated to examine the reliability of the proposed models with other models. The comparison of several data-driven regression methods indicate that the exponential Gaussian Process Regression (GPR) model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The GPR model was then used to predict the water level after sorting the data based on 10 days maximum and minimum values of the water level, and the results proved the success of this model in catching the extremes of the water levels. In addition to that, based on two uncertainty indicators, it was concluded that the proposed model, the GPR, was capable of predicting the water level of the river with high precision and less uncertainty where the computed using the 95% prediction uncertainty (95PPU) and the d-factor were found to be equal to 98.276 and 0.000525, respectively. The findings of this study show the efficacy of the GPR model in capturing the changes in the water level in a river. Due to the importance of the water level of a river being an parameter for flood monitoring, this technique is likely beneficial to the design of the mitigation strategies for future flooding events.

ABSTRACTPredicting the infiltration rate (IR) of treated wastewater (TWW) is essential in controlling clogging problems. Most researchers that predict the IR using neural network models considered the characteristics parameters of soil without considering  those of TWW. Therefore, this study aims to develop a model for predicting the IR based on various combinations of TWW characteristics parameters (i.e. total suspended solids (TSS), biological oxygen demand (BOD), electric conductivity (EC), pH, total nitrogen (TN), total phosphorous (TP), and hydraulic loading rate (HLR)) as input parameters. Therefore, two different artificial neural network (ANN) architectures, multilayer perceptron model (MLP) and Elman neural network (ENN), were used to develop optimal model. The optimal model was selected through evaluating three stages: selecting the best division of data, selecting the best model, and deciding the best combination of input parameters based on several performance criteria. The study concluded that the first combination of inputs that include all the seven-parameter using MLP model associated with 90% division of data was the optimal model in predicting the IR depending on TWW characteristics parameters, achieving a promising result of 0.97 for the coefficient of determination, 0.97 for test regression, 0.012 for MSE with 32.4 of max relative percentage error.Abbreviations:IR: Infiltration Rate; TWW: Treated Wastewater; TSS: Total Suspended Solids; BOD: Biological Oxygen Demand; EC: Electric Conductivity; HC: Hydraulic Conductivity; TN: Total Nitrogen; TP: Total Phosphorous; HLR: Hydraulic Loading Rate; ANN: Artificial Neural Network; MLP: Multilayer Perceptron Model; ENN: Elman Neural Network; FFANN: Feedforward Artificial Neural Networks; R: Regression Values; SAR: Sodium Adsorption Ratio; DOC: Dissolved Organic Carbon; ANAMMOX: Anaerobic Ammonium Oxidation; CEC: Cation Exchange Capacity; BPNN: Back Propagation Neural Network; GRNN: General Regression Neural Networks; ELM: Extreme Learning Machine Neural Networks; TDNN: Time Delay Neural Network; TLRN: Time Lag Recurrent Network; NGWTP: North Gaza Wastewater Treatment Plant; MASL: Meters Above Sea Level; DNC: Dynamic Node Creation; PWA: Palestinian Water Authority; RBF: Radial Basis Function; ANFIS: Adaptive Neuro Fuzzy Inference System; BD: Bulk Density; RMSE: Root Mean Square Error; MAE: Mean Absolute Error; MSE: Mean Square Error; R2: Determination Coefficient; LLR: Local Linear Regression; DLLR: Dynamic Linear Regression; MNN: Modular Neural Networks; RNN: Recurrent Neural Network; NARX: Nonlinear Autoregressive with Exogenous input network; WNN: Wavelet Neural Networks

Water shortage in arid and semi-arid land is one of the most important challenges of decision-makers. The seawater greenhouse (SWG) is a useful solution for water supply in the agriculture sector. The optimal design of a SWG with lower consumption of energy and higher freshwater production is a real challenge for the decision-makers. This study used two ensemble models and multiple multi-layer perceptron (MLP) models based on non-climate data to predict freshwater production energy consumption in the SWG. The Copula Bayesian average model (CBMA) was used to develop the BMA model using different copula functions and distributions. In the first level, multiple MLP models using the dimension of SWG as inputs predicted freshwater and energy consumption in a SWG. In the next level, The CBMA and BMA were used to predict freshwater production and energy consumption. The uncertainty analysis of outputs, use of new models and non-climate data are the novelties of the current study. The results indicated that the CBMA decreased the mean absolute error (MAE) value of the BMA, MLP-SEOA, MLP-SCA, MLP-BA, MLP-PSO, and MLP models by 2.7%, 19%, 31%, 40%, 41%, and 42%, respectively for predicting freshwater production. The root mean square error (RMSE) of the CBMA was 40%, 49%, 56%, 57%, 62%, and 64% lower than those of the BMA, MLP-SEOA, MLP-SCA, MLP-BA, MLP-PSO, and MLP models, respectively for predicting energy consumption. The uncertainty analysis indicated that the CBMA and BMA provided the lowest uncertainty among other models. The current study results indicated that the use of ensemble models improved the accuracy of individual models for predicting energy consumption and freshwater production. The findings of the study indicated that the ensemble models using the dimension of SWGs as inputs successfully predicted energy consumption and freshwater production in a SWG.

The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear nature of energy data. Conversely, meta-heuristic algorithms offer optimization capabilities but suffer from computational burdens, especially with high-dimensional data. This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. We analyze state-of-the-art techniques, innovations, and recent advancements, identifying open research challenges. Additionally, we propose a novel framework that seamlessly merges meta-heuristic algorithms into deep learning paradigms, aiming to enhance performance and efficiency in addressing energy-related problems. The contributions of the paper include:

Water is the most basic need for humans and a source of livelihood for humans. Lack of human awareness to maintain water quality, causing water to become polluted, by both industrial and household waste, impacts on human health and material loss. Thus, it is important to create technology that can monitor water pollution automatically and quickly. This research aims to create a system which utilizes the Internet of Things (IoT) technology that can facilitate quality of water by measuring parameters such as pH and turbidity. The methodology of the system progresses by the usage of a controller which is ATmega328P-AU, pH sensor to measure acidity, turbidity sensor to measure turbidity level, LPWAN LoRa works like a communication of data transmission as well as cloud service, namely Antares, to store data that are sent via Android. Based on the outcomes, the proposed system has achieved a reliable accuracy with percentage error of 99.73% in pH sensor and 99.41% in the turbidity sensor. Also, 2.6 s is the average required time to deliver the results to the cloud service.

Advanced human activities, including modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where household water is obtained from rivers after treatment. Increased concentrations of nitrogen compounds result in the suspension of household water supplies. Artificial Neural Networks (ANNs) have already been deployed for the prediction of nitrogen compounds in various countries. But standalone ANN have several limitations. However, the limitations of ANNs can be resolved using hybrid models. This study proposes a new ACO-ENN hybrid model developed by integrating Ant Colony Optimization (ACO) with an Elman Neural Network (ENN). The developed ACO-ENN hybrid model was used to improve the prediction results of nitrate-nitrogen and ammonia-nitrogen prediction models. The results of new hybrid models were compared with multilayer ANN models and standalone ENN models. There was a significant improvement in the mean square errors (MSE) (0.196→0.049→0.012, i.e. ANN→ENN→Hybrid), mean absolute errors (MAE) (0.271→0.094→0.069) and Nash–Sutcliffe efficiencies (NSE) (0.7255→0.9321→0.984). The hybrid model had outstanding performance compared with the ANN and ENN models. Hence, the prediction accuracy of nitrate-nitrogen and ammonia-nitrogen has been improved using new ACO-ENN hybrid model.

Development, operations and management of multi-objective reservoirs, is vital for timely water supply. Optimisation studies were done at the Klang Gate Dam (KGD) utilising standard optimisation and dynamic programming; according to the technology then. Taking it further, the KGD was studied using the nature-inspired meta-heuristic algorithms (MHAs). The Whale Optimisation Algorithm (WOA) solves complex technical issues. The Lévy flight and distribution (LFWOA) was incorporated to increase productivity at the KGD. The aim of this study to minimise KGD's water deficit with WOA and LFWOA. The study comprises of two sections. The first section examined observed monthly inflow, demand, and storage data from 2001 to 2019, whilst the second compares performances to established MHAs, from 1097 to 2008.  In the first section, LFWOA and WOA reliability were 60.53 and 58.33 %,respectively. For 2010, both methods gave similar vulnerability value. The LFWOA scored 1.04 while the WOA scored 1. In second section, the LFWOA satisfied 69.70% of exact demand, while the WOA met 56.06%. LFWOA had attained the least shortage. The LFWOA algorithm was the most robust among the MHAs (1.88). In short, the LFWOA is better on the count of reliability, resilience, and scarcity scores.

To accurately predict tropospheric ozone concentration(O3), it is needed to investigate the variety of artificial intelligence techniques’ performance, such as machine learning, deep learning and hybrid models. This research aims to effectively predict the hourly ozone trend via fewer input variables. This ozone prediction attempt is performed on diversity data of air pollutants (NO2, NOx, CO, SO2) and meteorological parameters (wind-speed and humidity). The historical datasets are collected from 3 sites in Malaysia. The study’s methodology progressed in two paths: standalone and hybrid models where hourly-averaged datasets are applied based on 5-time horizon analysis scenario, with different inputs’ combinations. For evaluation, all models are tested throughout 5-performance indicator and illustrated on Modified Taylor diagram. Sensitivity analysis of input variables is quantified. Additionally, uncertainty analysis is conducted to assess their confidence level associated with Willmott Index. Based on R2, results indicated that XGBoost has higher accuracy compared to MLP and SVR; meanwhile, LSTM and CNN outweighs XGBoost. In terms of robustness and accuracy, the proposed hybrid model possesses superlative performance compared to all above-mentioned techniques. The proposed model achieved exceptional results as the highest R2, the highest 95% confidence degree, and narrower confidence interval width, are 93.48%, 98.16%, and 0.0014195, respectively.

This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia.

Water Quality Index (WQI) is the most common determinant of the quality of the stream-flow. According to the Department of Environment (DOE, Malaysia), WQI is chiefly affected by six factors, which are, chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), suspended solids (SS), -potential for hydrogen (pH), and ammoniacal nitrogen (AN). In fact, understanding the inter-relationships between these variables and WQI can improve predicting the WQI for better water resources management. The aim of this study is to create an input approach using ANNs (Artificial Neural Networks) to compute the WQI from input parameters instead of using the indices of the parameters when one of the parameters is absent. The data are collected from the nine water quality monitoring stations at the Klang River basin, Malaysia. In addition, comprehensive sensitivity analysis has been carried out to identify the most influential input parameters. The model is based on the frequency distribution of the significant factors showed exceptional ability to replicate the WQI and attained very high correlation (98.78%). Furthermore, the sensitivity analysis showed that the most influential parameter that affects WQI is DO, while pH is the least one. Additionally, the performance of models shows that the missing DO values caused deterioration in the accuracy.

High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R2values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.

Natural calamities like droughts have harmed not just humanity throughout history but also the economy, food, agricultural production, flora, animal habitat, etc. A drought monitoring system must incorporate a study of the geographical and temporal fluctuation of the drought characteristics to function effectively. This study investigated the space–time heterogeneity of drought features across Sabah and Sarawak, East Malaysia. The Standardized Precipitation Index (SPIs) at timescales of 1-month, 3-months, and 6-months was selected to determine the spatial distribution of drought characteristics. Rainfall hydrographs for the area for 30 years between 1988 and 2017 have been used in this study. A total of six five-year sub-periods were studied, with an emphasis on the lowest and highest drought occurrence. The sub-periods were a division of the 30 years over an arbitrary continual division for convenience. The results showed that the sub-periods 1993–1997 and 2008–2012 had the highest and lowest comparative drought events. The drought conditions were particularly severe in Central and Eastern  parts of East Malaysia, owing to El Nino events and the country's hilly terrain. Understanding how and when drought occurs can aid in establishing and developing drought mitigation strategies for the region.

Accurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (°C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regression; mean absolute, relative and maximum error) and Taylor diagram.

Concrete is the most utilized material (i.e., average production of 2 billion tons per year) for the construction of buildings, bridges, roads, dams, and several other important infrastructures. The strength and durability of these structures largely depend on the compressive strength of the concrete. The compressive strength of concrete depends on the proportionality of the key constituents (i.e., fine aggregate, coarse aggregate, cement, and water). However, the optimization of the constituent proportions (i.e., matrix design) to achieve high-strength concrete is a challenging task. Furthermore, it is essential to reduce the carbon footprint of the cementitious composites through the optimization of the matrix. In this research, machine learning algorithms including regression models, tree regression models, support vector regression (SVR), ensemble regression (ER), and gaussian process regression (GPR) were utilized to predict the compressive and tensile concrete strength. Also, the model performance was characterized based on the number of input variables utilized. The dataset used in this research was compiled from journal publications. The results showed that the exponential GPR had the highest performance and accuracy. The model had an impressive performance during the training phase, with a R2of 0.98, RMSE of 2.412 MPa, and MAE of 1.6249 MPa when using 8 input variables to predict the compressive strength of concrete. In the testing phase, the model maintained its accuracy with a R2of 0.99, RMSE of 0.0025134 MPa, and MAE of 0.0016367 MPa. In the training and testing phases, the exponential GPR also demonstrated high accuracy in predicting the tensile strength with an R2, RMSE, and MAE of 0.99, 0.00049247 MPa, and 0.00036929 MPa, respectively. Furthermore, in the prediction of tensile strength the number of variables utilized had an insignificant effect on the performance of the models. However, in predicting the compressive strength, an increase in the number of input variables lead to an enhancement in the performance metrics. The results of this research can allow for the quick and accurate prediction of the strength of a given concrete mixture design.

Sediment transport is a normal phenomenon in rivers and streams, contributing significantly to ecosystem production and preservation by replenishing vital nutrients and preserving aquatic life’s natural habitats. Thus, sediment transport prediction through modeling is crucial for predicting flood events, tracking coastal erosion, planning for water supplies, and managing irrigation. The predictability of process-driven models may encounter various restrictions throughout the validation process. Given that data-driven models work on the assumption that the underlying physical process is not requisite, this opens up the avenue for AI-based model as alternative modeling. However, AI-based models, such as ANN and SVM, face problems, such as long-term dependency, which require alternative dynamic procedures. Since their performance as universal function approximation depends on their compatibility with the nature of the problem itself, this study investigated several distinct AI-based models, such as long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM), in predicting sediment transport in the Johor river. The collected historical daily sediment transport data from January 1, 2008, to December 01, 2018, through autocorrelation function, were used as input for the model. The statistical results showed that, despite their ability (deep learning and machine learning) to provide sediment predictions based on historical input datasets, machine learning, such as ANN, might be more prone to overfitting or being trapped in a local optimum than deep learning, evidenced by the worse in all metrics score. With RMSE = 11.395, MAE = 18.094, and R2 = 0.914, LSTM outperformed other models in the comparison.

For decision-making in farming, the operation of dams and irrigation systems, as well as other fields of water resource management and hydrology, evaporation, as a key activity throughout the universal hydrological processes, entails efficient techniques for measuring its variation. The main challenge in creating accurate and dependable predictive models is the evaporation procedure's non-stationarity, nonlinearity, and stochastic characteristics. This work examines, for the first time, a transformer-based deep learning architecture for evaporation prediction in four different Malaysian regions. The effectiveness of the proposed deep learning (DL) model, signified as TNN, is evaluated against two competitive reference DL models, namely Convolutional Neural Network and Long Short-Term Memory, and with regards to various statistical indices using the monthly-scale dataset collected from four Malaysian meteorological stations in the 2000–2019 period. Using a variety of input variable combinations, the impact of every meteorological data on the Epforecast is also examined. The performance assessment metrics demonstrate that compared to the other benchmark frameworks examined in this work, the developed TNN technique was more precise in modelling monthly water loss owing to evaporation. In terms of predictive effectiveness, the proposed TNN model, enhanced with the self-attention mechanism, outperforms the benchmark models, demonstrating its potential use in the forecasting of evaporation. Relating to application, the predictive model created for Epprojection offers a precise estimate of water loss due to evaporation and can thus be used in irrigation management, agriculture planning based on irrigation, and the decrease in fiscal and economic losses in farming and related industries where consistent supervision and estimation of water are considered necessary for viable living and economy.

Coastal zone has high value of socioeconomic activities. The number of developments and activities at the coastal areas of various sectors has increased day by day. Tsunami hazard is classified as one of the most devastating natural disasters that cause destruction along the coastal areas resulted to human fatalities. There are many causes of tsunami, but the most common is due to earthquake in the ocean. Previously recognised as a safe zone from natural disasters of tsunami, Malaysia was confronted by a rude awakening of the 2004 Andaman tsunami, causing a total loss of 68 lives. However, the potential impacts of tsunamis towards Malaysia coastal areas, especially at East Coast of Peninsular Malaysia, have not been explored extensively. This study, therefore, is critical and pertinent. It aims to investigate the potential seismic activities that generate tsunami occurrences affecting East Coast Peninsular Malaysia. This study further simulates Manila Trench earthquake-induced tsunami at various earthquake intensities, namely Mw 7.0, 8.0 and 9.0, by using TUNA model. Results obtained from this study indicated that tsunami wave generated by Manila Trench earthquake at Mw 9.0 resulted to the highest disastrous effects towards East Coast of Peninsular Malaysia. Findings from this study have established that Kelantan will experience the highest inundation depth at 4.0 m in Pasir Puteh with the highest inland inundation distance at 5.5 km in Bachok. Besides that, Terengganu will experience the highest inundation depth at 6.0 m in Kuala Terengganu with the highest inland inundation distance at 6.0 km also in Kuala Terengganu. In Pahang, the highest inundation depth at 4.5 m with the highest inland inundation distance at 3.3 km is anticipated to experience in Pekan, while in Johor, the highest inundation depth at 1.2 m with the highest inland inundation distance at 0.4 km is anticipated to experience in Mersing. On the other hand, Mw 8.0 and 7.0 earthquake was expected to be insignificant upon arriving at the coastal areas. Mitigation measures and evacuation plans can be effectively planned as findings obtained from this study are able to provide vital information on the possibilities of tsunami waves occurrences to overcome massive losses of properties and life.

One of the largest hydropower facilities currently in operation in Malaysia is the Terengganu hydroelectric facility. As a result, for hydropower generation to be sustainable, future water availability in hydropower plants must be known. Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall patterns. Finding the best value for the hyper-parameters is one of the problems with machine learning algorithms, which have lately been adopted by many academics. In this research, Artificial Neural Network (ANN) is integrated with a nature-inspired optimizer, namely Cuckoo search algorithm (CS-ANN). The performance of the proposed algorithm then will be examined based on statistical indices namely Root-Mean-Square Error (RSME) and Determination Coefficient (R2). Then, the accuracy of the proposed model will be then examined with the stand-alone Artificial Neural Network (ANN). The statistical indices results indicate that the proposed Hybrid CS-ANN model showed an improvement based on R2value as compared to ANN model with R2of 0.900 at training stage and R2of 0.935 at testing stage. RMSE value, for ANN model, is 127.79 m3/s for training stage and 12.7 m3/s at testing stage. While for the proposed Hybrid CS-ANN model, RMSE value is equal to 121.7 m3/s for training stage and 10.95 m3/s for testing stage. The results revealed that the proposed model outperformed the stand-alone model in predicting the river flow with high level of accuracy. Although the proposed model could be applied in different case study, there is a need to tune the model internal parameters when applied in different case study.

Hydrologists rely extensively on anticipating river streamflow (SF) to monitor and regulate flood management and water demand for people. Only a few simulation systems, where previous techniques failed to anticipate SF data quickly, let alone cost-effectively, and took a long time to execute. The bat algorithm (BA), a meta-heuristic approach, was used in this study to optimize the weights and biases of the artificial neural network (ANN) model. The proposed hybrid work was validated in five different study areas in Malaysia. The statistical tests analysis of the preliminary results revealed that hybrid BA-ANN was superior to forecasting the SF at all five selected study areas, with average RMSE values of 0.103 m3/s for training and 0.143 m3/s for testing as compared to ANN standalone training and testing yielding 0.091 m3/s and 0.116 m3/s, respectively. This finding signifies that the implementation of BA into the ANN model resulted in a 20% improvement. In addition, with an R2score of 0.951, the proposed model showed a better correlation than the 0.937 value of R2of standard ANN. Nonetheless, while the proposed work outperformed the conventional ANN, the Taylor diagram, violin plot, relative error, and scatter plot findings confirmed the disparities in the proposed work’s performance throughout the research regions. The findings of these evaluations highlighted that the adaptability of the proposed works would need detailed investigation because its performance differed from case to case.

Streamflow forecasting has always been important in water resources management, particularly the peak flow, which often determines the seriousness of the impending flood. However, the highly imbalanced flow distribution often hinders the machine learning algorithm's performance. In this paper, streamflow forecasting was approached through the formulation of two distinct machine learning problems: categorical streamflow forecast and regression streamflow forecast. Due to the distinctive characteristics of these two adopted forms, selecting the correct algorithm for the machine learning problem along with their hyperparameter tuning process is critical to the realization of the desired results. For the distinct streamflow formulated scenarios, three neural network algorithms and their hyperparameter tuning strategy were investigated. The comparative empirical studies had revealed that formulated categorical-based streamflow forecast is a better choice than a regression-based streamflow forecast, regardless of the algorithms used; for instance, the f1-score of 0.7 (categorical based) is obtained compared to the 0.53 (regression based) for the LSTM in scenario 1 (binary). Furthermore, forest-based algorithms were investigated and shown to be superior at forecasting high streamflow fluctuations in situations featuring low-dimensional streamflow input. Besides, encoding the streamflow time series as images (input) for forecasting purposes would require a thorough analysis as there is a discrepancy in the results, revealing that not all approaches are suitable for streamflow image transformation. The functional ANOVA analysis provided evidence to substantiate the Bayesian optimization results, implying that the hyperparameters were effectively optimized.

Given the large manufacturing and consumption of concrete globally, there has been widespread speculation that it is a significant source of greenhouse gas emissions. Consequently, scientists are looking for sustainable and beneficial solutions to the environment. The influence of adding expanded clays (Leca) and crushed recycled bricks clay lightweight aggregates (RBA) on the fly ash and glass powder-based geopolymer concrete were investigated at ambient and high temperatures. High temperature influences the significant characteristics of mortar and lightweight aggregate geopolymer concrete. Low calcium fly ash was utilized as a binder in the concrete paste with a 10% replacement rate of Glass Powder. The concrete specimens were heated to 200, 400, 550, and 800 °C at a 7 °C/min rate for 60 min. Lower coarse aggregate strength is a contributing factor to concrete compressive strength reductions. The influences of high temperatures on geopolymer concrete were studied in terms of mass loss, cracking, water absorption, and microstructure. The research outcomes show that the geopolymer concrete suffered damage and dehydration at a heating temperature of 550 °C and above. Acceptable correlation between the temperature degree and Ultrasonic Pulse Velocity UPV with the compressive strength of the lightweight concrete. The test results showed the residual compressive strength 110.2%, 92.9%, 71.9%, and 55.6% as well as 109.2%, 91.5%, 79.6%, and 52.6% in addition to the density reduced 0.17%, 2.56%, 7.29%, and 12.18%, and 0.74%, 3.97%, 6.31%, and 13.25% in 200,400,550, and 800 °C for Leca and RBA type respectively. The equations showed acceptable results with experimental data. The impact of replacing waste glass powder with fly ash on the development of microstructures and gels was also investigated.

The optimization of dam reservoir operations is of the utmost importance, as operators strive to maximize revenue while minimizing expenses, risks, and deficiencies. Metaheuristics have recently been investigated extensively by researchers in the management of dam reservoirs. But the animal-concept-based metaheuristic algorithm with Lévy flight integration approach has not been used at Karun-4. This paper investigates the optimization of dam reservoir operation using three unexplored metaheuristics: the whale optimization algorithm (WOA), the Levy-flight WOA (LFWOA), and the Harris hawks optimization algorithm (HHO). Utilizing a time series data set on the hydrological and climatic characteristics of the Karun-4 hydroelectric reservoir in Iran, an analysis was conducted. The objective functions and constraints of the Karun-4 hydropower reservoir were examined throughout the optimization procedure. HHO produces the best optimal value, the least-worst optimal value, the best average optimal value, and the best standard deviation (SD) with scores of 0.000026, 0.001735, 0.000520, and 0.000614, respectively, resulting in the best overall ranking mean (RM) with a score of 1.5 at Karun-4. Throughout the duration of the test, the optimized trends of water release and water storage indicate that HHO is superior to the other investigated metaheuristics. WOA has the best correlation of variation (CV) with a score of 0.090195, while LFWOA has the best convergence rate (3.208 s) and best CPU time. Overall, it can be concluded that HHO has the most desirable performance in terms of optimization. Yet, current studies indicate that both WOA and LFWOA generate positive and comparable outcomes.

Malaysia reported its first COVID-19 case on January 25, 2020, and the cases have continued to grow, necessitating the implementation of additional measures. Hence, determining the factors responsible for the significant increase in COVID-19 cases is the top priority issue for the government to take necessary action and ultimately restrain this virus before the vaccine availability. Researchers had predicted that air pollution had an indirect relationship with COVID-19 in terms of virus infections. As a result, this study focuses on the link between the Air Pollutant Index (API) and COVID-19 infections. The initial data set consists of daily confirmed COVID-19 cases in Malaysia and API readings obtained from the Ministry of Health (MOH) and the Department of the Environment (DOE). The results show that Klang (S22) recorded the highest mean of API which at 62.70 while the lowest is at Limbang (S37) (25.37). Next, due to the implementation of Movement Control Order (MCO) in Malaysia and reducing social movement, 27 stations recorded a good level of API compare to the stations that recorded moderate and unhealthy levels. There is positive relationship between API and COVID-19 at each of the region which are North 0.4% (R2=0.004), Central 2.1% (R2=0.021), South 0.04% (R2=0.0004), East 1.6% (R2=0.016), Sarawak 0.2% (R2=0.002), meanwhile Sabah recorded negative correlation at 4.3% (R2=0.043). To conclude, the API value did not have a strong relationship with the rising number of COVID-19 daily cases.

Power supply is a key issue for decision-makers. The reservoir operation of multi-reservoir systems is an important aspect to consider in efforts to increase power generation. This research studies a multi-reservoir system comprising of the Khersan-I (KHI), Karoon-III (KAIII) and Karoon-IV (KAIV) with the intent being to increase power generation. To achieve this, the Two-Point Heading Rule was integrated with a new optimization algorithm, namely the Seagull Optimization Algorithm (SEOA). The Two-Point Heading Rule was used based on four distinct scenarios, namely Two-Point Heading Rule (1), Two-Point Heading Rule (2), Two-Point Heading Rule (3) and Two-Point Heading Rule (4). The Seagull Optimization Algorithm Seagull Optimization Algorithm was then used to find two heading parameters of the TPHRs. The Seagull Optimization Algorithm was subsequently benchmarked against the Salp Swarm Algorithm (SSA), Bat Algorithm (BA) and the Shark Optimization Algorithm (SOA). Various inflow scenarios consisting of the first inflow scenario (dry condition), the second inflow scenario (normal) and the third inflow scenario (wet condition) were considered for the optimal operation of this multi-reservoir system. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the first inflow scenario and was 3.22 while the average solution of Seagull Optimization Algorithm, Salp Swarm Algorithm, Shark Optimization Algorithm, and Bat Algorithm in respective order was 3.25, 3.93, 4.87 and 6.03. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the second inflow scenario was 2.14 while the average best solution of Seagull Optimization Algorithm, Salp Swarm Algorithm, Shark Optimization Algorithm, and Bat Algorithm in respective order was 2.16, 2.98, 3.96, and 4.89. It can be concluded that the SEOA outperformed all of the other algorithms. It was also found that the SEOA based on the Two-Point Heading Rule (3) under the third inflow scenario provided the most power generation for the KHI and KAIV systems. A multi-criteria decision was utilized to choose the best algorithm and heading policy. The ensuing results indicate that the SEOA had the best performance out of all the algorithms based on Two-Point Heading Rule (3) and the third inflow scenario.