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Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).

Ammonium is known to be one of the most significant pollutants in water bodies. The presence of ammonium in water is mainly originated from agricultural activities, domestic sewage and industrial effluent. This study evaluates the performance of two commercial thin film composite (TFC) membranes, i.e., NF270 and XLE from FilmTec™ for ammonium removal using synthetic wastewater and domestic sewage. The filtration experiment was conducted at different feed ammonium concentrations, humic acid concentrations, pHs and pressure. Results showed that the membrane rejection against ammonium increased dramatically with increasing ammonium concentration. However, the membrane flux was slightly compromised at higher ammonium concentration. With respect to pH, highest ammonium removal rate was able to be achieved at an optimum pH of 10. Besides, the permeation flux increased gradually with increasing feed pressure. From the results, the XLE membrane outperformed the NF270 membrane in terms of ammonium rejection. The retentate of XLE membrane filtration process was found to be useful as liquid fertiliser for plant growth. The results indicated that the TFC membrane process is not only able to produce permeate with an ammonium concentration below the acceptable limit of 10 mg/L but also able to produce retentate with enriched ammonium for plant growth.

The main focus of this review is to investigate the potential of small-scale solar desalination as a viable freshwater source by comparing the performance of different types of modification on sola...

Membrane fouling has been claimed as a significant culprit ruining all membrane processes. Feed spacer is a component that helps to mitigate the membrane fouling by creating turbulence over the feedwater flow. In recent years, three-dimensional (3D) printing technology has been adopted to produce feed spacers with complex geometry as well as maintaining high accuracy. Despite the fact that common 3D printing techniques and materials have been studied by numerous researchers, the contributing factors towards the improvement of feed spacers efficiency for membrane filtration systems in water treatment remains unclear. In fact, the performance of feed spacers are highly associated with their mechanical strength, the water flux produced and the fouling resistance of membrane sheets when tested simultaneously with the feed spacers. These characteristics are closely related to membrane fouling and they can be influenced by the 3D printing materials, methods and designs. This paper provides updates on the progress of 3D printed feed spacer development related to how the 3D printing process works, the materials associated with the 3D printing methods and how the spacer designs affect the membrane separation processes.

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.

Nafion based proton exchange membrane (PEM) has long been used as conventional PEM in direct methanol fuel cell (DMFC) industry. However, the high cost of Nafion membrane and other drawbacks like high methanol crossover hinder the advancement of this industry. This study aims to develop a low cost membrane using sulfonated poly ether ether ketone (SPEEK) polymer. Silica and silicotungstic acid (SiWA) were incorporated into the membrane matrix using solution casting method. The optimum loading of the additives was tuned and it is discovered that the SPEEK membrane containing 10 wt% of silica and 5 wt% of SiWA has the best performance due to its high proton conductivity and moderately low methanol permeability. The performance of the membrane can further be enhanced by adding (3-aminopropyl)triethoxysilane (APTES) and carbonyldiimidazole (CDI) as coupling agents. Inclusion of APTES and CDI in SPEEK could not only improve the compatibility between organic SPEEK and inorganic additives, but also improve the homogeneity and dispersity of the additives. As a result, the resultant membrane with a better dimensional stability achieves high selectivity (10.60 × 104 S.s/cm3) up to 6.5 times more than pristine SPEEK membrane and 1.3 times higher than the commercial Nafion 117 membrane.

A strong understanding of severe drought conditions is important for its mitigation and damage alleviation. Given the Peninsular Malaysia’s drought vulnerability and its progressively increasing temperatures in the future, this study assessed the significance of temperature for the drought formation through temporal pattern, spatial characteristic and operational accuracy indicated by the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at the timescales of 1-, 3- and 6-month. Temporal analyses of drought frequency and fluctuations of the SPI and SPEI showed similar changes in moisture responsiveness over the increasing timescales. However, in terms of the number of dry months, the two indices showed different trends, consequential of the influence of temperature in the SPEI. The interchangeability of the two indices was confirmed through spatial variation analysis of drought frequency, mean drought duration, mean drought severity and mean drought peak. From an occurrence, duration and onset detection accuracy consideration, the SPI is better for the 1-month short-term drought, while the SPEI is better for the 3-month mid-term and 6-month long-term droughts. This is a result of the increased significance of temperature in drought formations. Further evaluations on drought severity also showed that the SPEI had better description of the long-term drought over Peninsular Malaysia during the 1997/1998 and 2015/2016 El-Nino drought events.

The capillary barrier system is a widely researched method used to control rainfall infiltration into soil slopes for mitigating rainfall-induced landslides. Conventional capillary barrier systems, however, are subjected to several weaknesses, such as the inability of the upper fine layer to function effectively under intense or prolonged rainfall, and sliding failure or erosion may occur in the fine layer as a result of excessive lateral seepage. This study aims to investigate the feasibility of using biomediated soil cover in a capillary barrier system to minimize rainfall infiltration into a residual soil slope. Firstly, the engineering properties of the original and biomediated residual soils were investigated. Secondly, an instrumented one-dimensional physical soil column was set up to investigate the infiltration behaviour of the tropical residual soil with and without biomediated soil cover. A numerical seepage model was simulated to compare the experimental and numerical results, as well as to verify the input parameters of the numerical simulation. Lastly, a two-dimensional slope model was simulated to investigate the effectiveness of the biomediated soil cover in minimizing infiltration under both intense (1-h, 4-h, 8-h, 24-h extreme rainfalls) and prolonged (72-h extreme rainfall) rainfall conditions. The results showed that the soil column with biomediated soil cover could effectively maintain the soil in an unsaturated state for a longer period of infiltration (i.e., 60 min) as compared with the original residual soil (i.e., 10 min only). The numerical simulation results agreed reasonably well with the experimental findings. The two-dimensional seepage analysis results indicated that the slopes with biomediated soil cover could reduce the infiltration of water into the underlying soil slope, and hence resulted in a shallower wetting front, particularly under short and intense extreme rainfall conditions.

Reservoir operation optimisation secures benefits, such as optimising energy production while minimising the possibility of flooding, operating costs, and water scarcity, at the lowest possible cost. This paper carries reviews of research on reservoir optimisation models and the consequential challenges of optimally operating reservoir operations. An introductory section is given to the background of reservoir operations and the current concerns on the optimal reservoir operations, for the decision-makers and stakeholders. Next, the review covered the recent ten years (between 2011 and 2021), on the recent research developments in innovation and techniques of reservoir operation optimisation. Further reviews on the conventional techniques that are the traditional methods, linear programming, nonlinear programming, and dynamic programming are discussed. Enhancements to the techniques in improving the drawbacks of the traditional techniques in optimisation of reservoir policies are next explained and evaluated. Recent advances in applying metaheuristics optimisation algorithms beneficial to the reservoir operations are explained, including the advantages and hinderances. A comprehensive tabulated and categorised review according to the classification of reservoir models, evaluation methods, and reservoir systems is given.

Drought’s frequency, duration and severity are essentials for impact control and mitigation of droughts. This paper adopts 3-month seasonal scale Streamflow Drought Index (SDI-3) to analyze these drought characteristics for the tropical climate at the Central, Southern and East Coast regions of Peninsular Malaysia in 1997-2016. The identified drought events were then evaluated using four major historical events from five different locations. The results showed that SDI-3 identified the occurrences of all reported drought events but with deviated onset and duration. However, the SDI-3 was concluded to be suitable for hydrological drought monitoring with reasons of increased temperature, groundwater and forest replenishment, and human factors. The spatial analysis on the drought characteristics also showed that most of the areas are more prone to short-term droughts, with relatively higher severity at north part of the Central Region, north-west part of the Southern Region and central part of the East Coast.

Understanding the operational definition of drought is important for alleviating the negative impacts of droughts. This study investigates the use of streamflows to indicate seasonal hydrological droughts in the Northern Region of Peninsular Malaysia. The seasonal 3-month Streamflow Drought Index (SDI-3) was used to analyse drought characteristics in 2003–2017. The occurrences of all three reported droughts were successfully identified with the SDI-3, with the identified events having a 3-month earlier onset with a 6-month longer duration; a 1-month delayed onset with a 5-month longer duration; and a 1-month delayed onset with a 1-month shorter duration, respectively compared to their actual onset and duration. Along with justifications such as the increase in evaporation and evapotranspiration due to temperature increase, losses for groundwater replenishment and human factors, the SDI-3 was concluded to be suitable for hydrological drought monitoring. Drought characteristics were spatially interpolated using the Inverse Distance Weighting (IDW) method, which was cross-validated for applicability with average root mean squared error (RMSE) and R equal to 0.630 and 0.586, respectively. It was found that most of the areas are more prone to short-term droughts with mean severity up to the mild category, except for the central zone which showed more severe droughts, albeit being less likely to occur.

In this study, a quantitative performance of three commercial polyamide nanofiltration (NF) membranes (i.e., NF, NF90, and NF270) for phosphorus removal under different feed conditions was investigated. The experiments were conducted at different feed phosphorus concentrations (2.5, 5, 10, and 15 mg·L−1) and elevated pHs (pH 1.5, 5, 10, and 13.5) at a constant feed pressure of 1 MPa using a dead-end filtration cell. Membrane rejection against total phosphorus generally increased with increasing phosphorus concentration regardless of membrane type. In contrast, the permeate flux for all the membranes only decreased slightly with increasing phosphorus concentration. The results also showed that the phosphorus rejections improved while water flux remained almost unchanged with increasing feed solution pH. When the three membranes were exposed to strong pHs (pH 1.5 and 13.5) for a longer duration (up to 6 weeks), it was found that the rejection capability and water flux of the membranes remained very similar throughout the duration, except for NF membrane with marginal decrement in phosphorus rejection. Adsorption study also revealed that more phosphorus was adsorbed onto the membrane structure at alkaline conditions (pH 10 and 13.5) compared to the same membranes tested at lower pHs (pH 1.5 and 5). In conclusion, NF270 membrane outperformed NF and NF90 membranes owing to its desirable performance of water flux and phosphorus rejection particularly under strong alkali solution. The NF270 membrane achieved 14.0 L·m−2·h−1 and 96.5% rejection against 10 mg·L−1 phosphorus solution with a pH value of 13.5 at the applied pressure of 1 MPa.

Drought is a harmful and little understood natural hazard. Effective drought prediction is vital for sustainable agricultural activities and water resources management. The support vector regression (SVR) model and two of its enhanced variants, namely, fuzzy-support vector regression (F-SVR) and boosted-support vector regression (BS-SVR) models, for predicting the standardized precipitation evapotranspiration indices (SPEI) (in this case, SPEI-1, SPEI-3 and SPEI-6, at various timescales) with a lead time of one month, were developed to minimize potential drought impact on oil palm plantations at the downstream end of the Langat River Basin, which has a tropical climate pattern. Observed SPEIs from periods 1976 to 2011 and 2012 to 2015 were used for model training and validation, respectively. By applying the MAE, RMSE, MBE and R2 as model assessments, it was found that the F-SVR model was best with the trend of improving accuracy when the timescale of the SPEIs increased. It was also found that differences in model performance deteriorates with increased timescale of the SPEIs. The outlier reducing effect from the fuzzy concept has better improvement for the SVR-based models compared to the boosting technique in predicting SPEI-1, SPEI-3 and SPEI-6 for a one-month lead time at the downstream of Langat River Basin.

This study produced a novel polysulfone (PSF) membrane for dye removal using lemon-derived carbon quantum dots-grafted silver nanoparticles (Ag/CQDs) as membrane nanofiller. The preparation of CQDs was completed by undergoing hydrothermal treatment to carbonize the pulp-free lemon juice into CQD solution. The CQD solution was then coupled with Ag nanoparticles to form Ag/CQDs nanohybrid. The synthesized powders were characterized in terms of morphologies, functional groups and surface charges. A set of membranes was fabricated with different loadings of Ag/CQDs powder using the nonsolvent-induced phase separation (NIPS) method. The modified membranes were studied in terms of morphology, elemental composition, hydrophilicity and pore size. In addition, pure water flux, rejection test and fouling analysis of the membranes were evaluated using tartrazine dye. From the results, 0.5 wt % of Ag/CQD was identified as the optimum loading to be incorporated with the pristine PSF membrane. The modified membrane exhibited an excellent pure water permeability and dye rejection with improvements of 169% and 92%, respectively. In addition, the composite membrane also experienced lower flux decline, higher reversible fouling and lower irreversible fouling. This study has proven that the addition of CQD additives into membrane greatly improves the polymeric membrane&rsquo, s properties and filtration performance.

Droughts are prolonged precipitation-deficient periods, resulting in inadequate water availability and adverse repercussions to crops, animals and humans. Drought forecasting is vital to water resources planning and management in minimizing the negative consequences. Many models have been developed for this purpose and, indeed, it would be a long process for researchers to select the best suited model for their research. A timely, thorough and informative overview of the models' concepts and historical applications would be helpful in preventing researchers from overlooking the potential selection of models and saving them considerable amounts of time on the problem. Thus, this paper aims to review drought forecasting approaches including their input requirements and performance measures, for 2007–2017. The models are categorized according to their respective mechanism: regression analysis, stochastic, probabilistic, artificial intelligence based, hybrids and dynamic modelling. Details of the selected papers, including modelling approaches, authors, year of publication, methods, input variables, evaluation criteria, time scale and type of drought are tabulated for ease of reference. The basic concepts of each approach with key parameters are explained, along with the historical applications, benefits and limitations of the models. Finally, future outlooks and potential modelling techniques are furnished for continuing drought research.

Over the past decade, there has been an increasing research on the use of machine learning tools for estimating reference crop evapotranspiration (ETo). However, due to the data-hungry nature of the machine learning models, all of these researches are not suitable for regions with limited data supply. This study aims to provide a breakthrough for the bottleneck through coupling of the inter-model ensemble with various data management schemes. The Bayesian modeling approach and a non-linear neural ensemble based inter-model ensemble (BMA-E and NNE-E) were developed locally with data from five different meteorological stations in the Peninsular Malaysia. The NNE-E was found to be highly robust spatially, whereby it can be used to estimate daily ETo accurately at other stations, even though with reduced input meteorological parameters. However, the performances of the locally trained models were found wanting and were fluctuating violently. This was resolved through creating a data pool that include the data from all stations and developing a universal NNE. By following the proposed scheme of things, the daily ETo can be easily estimated across the whole Peninsular Malaysia. This being, without the need for historical data and new models at estimation site.

Climatic parameters fluctuate dynamically and their turbulences become more significant as the influence of the climate change increases. A robust model that is able to factor in the recent climate change for long-term climatic parameters forecasting is desired to strategically plan for future anthropogenic activities. In this study, two stochastic time series model, namely the seasonal auto-regressive integrated moving average (SARIMA) model and the artificial neural network (ANN) model are used to predict monthly mean temperature (Tmean), relative humidity (RH), wind speed (u) and pan evaporation (Epan) up to 12 months ahead. This study is conducted using data collected from three meteorological stations in the northern region of the Peninsular Malaysia. The stochastic models forecasted the Tmean with the highest accuracy, followed by RH, u and Epan. Besides, despite the increasing time step (from 1 to 12 months), the accuracy of the models remain consistent. However, both of the models are susceptible to the occurrence of extreme climates. In general, the SARIMA model performs better than the ANN model, probably attributed to its ability to consider the seasonality of the climatic data rather than depending solely on black-box computation.

The Department of Environment (DOE) of Malaysia evaluates river water quality based on the water quality index (WQI), which is a single number function that considers six parameters for its determination, namely the ammonia nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). The conventional WQI calculation is tedious and requires all parameter values in computing the final WQI. In this study, the extreme learning machine (ELM) and the radial basis function kernel Gaussian process regression (GPR), were enhanced with bootstrap aggregating (bagging) and adaptive boosting (AdaBoost) for the WQI prediction at the Klang River, Malaysia. The global performance indicator (GPI) was used to evaluate the models’ performance. By preparing different input combinations for the WQI prediction, the parameter importance was found in following order: DO > COD > SS > AN > BOD > pH, and all models demonstrated lower prediction accuracy with a lesser number of parameter inputs. The GPR revealed a consistent trend with higher WQI prediction accuracy than ELM. The Adaboost-ELM works better than the bagged-ELM for all input combinations, while the bagging algorithm improved the GPR prediction under certain scenarios. The bagged-GPR reported the highest GPI of 1.86 for WQI prediction using all six parameter inputs.

Estimation of evapotranspiration (ET) is a challenging, yet important task as the ET value can be used to predict many other natural phenomena. In this work, the reference evapotranspiration (ET0) was estimated using the extreme learning machine (ELM) at two meteorological stations located in the northern region of the Straits of Malacca. Optimum designs of the ELM were first determined and it was found that the different number of hidden neurons and activation functions were favourable to various input combinations. In order to enhance the performance of the ELM, the bootstrap aggregating algorithm was integrated to resample the original dataset. However, the performance of bagged-ELM was found to be poorer than the base ELM. This could be attributed to the high stability of the base ELM model whereby the training size already overwhelmed the dimensionality of the problem itself. The bootstrap aggregation data fusion technique produced a “backfire” effect that degraded the accuracy and generalisability of the base ELM model.

This study investigated the characteristics of various graphene oxide (GO) nanohybrid membranes and their performance in algal organic matter (AOM) filtration. The membranes were fabricated by phase inversion method. The effect of GO and its nanohybrids embedded in membranes was investigated in terms of wettability, porosity, pore size, surface charge, composition, morphology, permeability, fouling resistance and antimicrobial ability. In addition, the rejection of protein and carbohydrate as critical foulants in AOM was studied. Based on the findings, all the composite membranes showed lower flux decline than PVDF membrane. Composite membranes maintained higher protein (81–86%) and carbohydrate (77–83%) rejection compared with PVDF membrane (64% for protein and 63% for carbohydrate). However, the reversible to irreversible fouling ratio of PVDF, ZnO/GO-PVDF, Ag/GO-PVDF and GO-PVDF membranes was 3.07, 1.53, 0.86 and 1.09, respectively. This scenario implied that more hydrophilic substances in small molecular weight (MW) contained in AOM had plugged the composite membranes' pores and resulted in irreversible fouling. On the other hand, ZnO/GO-PVDF and Ag/GO-PVDF membranes exhibited superior antimicrobial ability and showed great potential in anti-biofouling mitigation.

The incorporation of microporous mixed-matrix membranes (MMMs) with inorganic nanomaterials has created an opportunity for developing new types of polymeric-based membranes with improved physicochemical properties and filtration performance for water applications. This chapter will review the latest developments of MMMs being incorporated into a wide range of nanomaterials for water and wastewater treatment processes. More specifically, it will provide an overview of the effects of various functionalized nanomaterials (i.e., titanium-based nanomaterials, carbon-based nanoparticles, binary metal oxides, silicon dioxide, and metal-organic framework) on the properties of MMMs with respect to hydrophilicity, morphology, water flux, solute rejection, antifouling, and antibacterial resistances. The potential for using functionalized nanomaterials in adsorptive MMMs fabrication for heavy-metal ions removal process will also be reviewed and discussed.