Selangor River Research Articles

Multi-Drug Resistance Bacteria: A Case Study in Western Peninsular Malaysia Freshwaters

The emergence of antibiotic resistant bacteria in aquatic environment has led to an increase in waterborne health risks to an alarming extent. This study attempts to investigate the population of certain antibiotic resistant strains in Peninsular Malaysia. From the samples of 14 rivers from 7 different states in Peninsular Malaysia, a total of 203 isolates were successfully isolated. These are from rivers in Negeri Sembilan, Melaka, Selangor, Kuala Lumpur, Kedah, Kelantan and Terengganu with 35, 15, 37, 39, 24, 26 and 27 isolates, respectively. The isolates were tested for their susceptibility towards 6 broad spectrum of antibiotics which are gentamicin, ampicillin, rifampicin, chloramphenicol, tetracycline, and ciprofloxacin. A total of 117 isolates were identified to have more than 20% MAR index value with 47 of the isolates possess minimum MAR index value of 50%. Based on MAR index value, 59% of isolates are high risk threats indicating a serious emergence of antibiotic resistant bacteria in the rivers in Peninsular Malaysia. Isolates with MAR index value of more than 50% were selected for 16S rRNA gene sequencing for further identification.Based on 16s rRNA gene sequencing, the isolates are a mixture of pathogenic and commensals bacteria. This also implies that the environment especially rivers can be a reservoir for genetic jugglery

Implementation of Long-Short Term Memory Neural Network (LSTM) for Predicting The Water Quality Parameters in Sungai Selangor

Predictions of future events must be factored into decision-making. Predictions of water quality are critical to assist authorities in making operational, management, and strategic decisions to keep the quality of water supply monitored under specific criteria. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, the purpose of this paper is to develop and train a Long-Short Term Memory (LSTM) Neural Network to predict water quality parameters in the Selangor River. The primary goal of this study is to predict five (5) water quality parameters in the Selangor River, namely Biochemical Oxygen Demand (BOD), Ammonia Nitrogen (NH3-N), Chemical Oxygen Demand (COD), pH, and Dissolved Oxygen (DO), using secondary data from different monitoring stations along the river basin. The accuracy of this method was then measured using RMSE as the forecast measure. The results show that by using the Power of Hydrogen (pH), the dataset yielded the lowest RMSE value, with a minimum of 0.2106 at station 004 and a maximum of 1.2587 at station 001. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and revealed the future developing trend of water quality parameters, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of water parameters.

Environmental Flow Assessment of a Tropical River System Using Hydrological Index Methods

Tropical rivers and wetlands are recognized as one of the greatest and most abundant ecosystems in terms of ecological and social benefits. However, climate change, damming, overfishing, water pollution, and the introduction of exotic species threaten these ecosystems, which puts about 65% of river flow and aquatic ecosystems under a moderate to high level of threat. This paper aims to assess the environmental flow of the Selangor River based on the hydrological index method using the Global Environmental Flow Calculator (GEFC) and Indicators of Hydrological Alterations (IHA) software. The daily flow data collected by the Department of Irrigation and Drainage (DID), Malaysia, over a 60-year period (1960–2020) was used in this study to assess the Selangor River flow alterations. As per the results, the river flow has had two distinct periods over the last 60 years. In the first period, the river flows without any alteration and has a natural flow with high flood pulses and low flow pulses. While in the second, or post-impact, period, the flow of the river has a steady condition throughout the year with very little fluctuations between the dry and wet seasons of the year. From the overall comparison of the pre- and post-impact periods, it can be concluded that the minimum flow in the dry seasons of the year has increased, while the maximum flow has decreased in the monsoon seasons during the post-impact period. As a result, the Flow Duration Curve (FDC) and Environmental Management Class (EMC) analysis of the river flow recommends that the Selangor River be managed under EMC “C” to provide sufficient water for both human use and ecosystem conservation, which would also help to avoid a water level drop in the reservoirs. However, further holistic studies are suggested for a detailed analysis of the effects of the dams on aquatic biodiversity and ecosystem services in the Selangor River Basin.

SATELLITE REMOTE SENSING FOREST MANAGEMENT IN SELANGOR RIVER BASIN AREA FROM 1990 TO 2020

Forests are the primary ecosystem on earth that preserves natural resources. This paper aims to identify the forest loss percentage in the Selangor River Basin areas from 1990 to 2020. The objectives are to analyze (i) the pattern of forest loss in the Selangor River Basin area and (ii) the land-cover changes in the study area using a change detection analysis. Datasets of four clear cloud images were based upon to produce the land-cover map and identify the forest loss. Landsat images were employed to detect the land-cover changes. The Landsat images’ methodology of land-cover classification was unsupervised pixel-based classification methods using maximum likelihoods. The specific land cover includes forest, built-up area, bare land, vegetation, water bodies, and cloud. The result showed that the forest areas decreased from 108546.93 hectares in 1990 to 70239.33 hectares in 2020, representing a class change of 17.05 percent. A drastic increase was also noted in the built-up area from 1407.51 hectares in 1990 to 43128.27 hectares in 2020, and this category of land cover increased by 18.56 percent over the 30-year timeline. The factors contributing to the forest loss include anthropogenic factors as the most important cause of land cover change. Urbanization, a lack of forest guards, and a failure to implement policies were all identified as human activities causing the loss of the forest and the rise of the built-up area. A DPSIR framework was produced to provide the relevant indicators and allow policymakers to contribute input on environmental concerns of the future impact in policy development. This framework act as a contribution in efforts to conserve natural resources through policy formulation.

Application of Remote Sensing in Mangroves at the Surrounding of Sungai Selangor Estuary in Kuala Selangor

The mangrove forest ecosystem protects the land area from the tidal wave hence preventing the coastal areas and properties from severe damage. Mangroves provide valuable ecological services and goods, sediment retention, food sources of some animals, and stabilisation of the coastal areas. Unfortunately, the species have been experiencing an extensive loss in many parts of the world. This paper aims to detect the changes in mangrove forests and possible changes in the Selangor river basin area. The methodology uses remote sensing data via supervised classification on a maximum likelihood algorithm to analyse the distribution of mangrove forests at the Selangor River basin for a thirty-two-year period, from 1989 to 2021. The findings indicate that the percentage of mangroves in the study area has reduced over the study period. The coverage of mangroves has reduced from 24.29 percent (1989) to 15.57 percent in 2008, and continued to reduce to 13.12 percent in 2021. The research finding indicates a decrease in mangroves due to aquaculture, tourism, agriculture, and other human activities. Such a trend may risk coastal and river erosion, thus necessitating a revision of the management policies for environmental protection.
 Keywords: mangrove, forest, remote sensing, Selangor river basin

Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia.

Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.

Influence of Dam to Rainfall-Runoff Response in a Tropical Climate – A Case Study of Selangor River Basin, Malaysia

This paper discusses the rainfall-runoff response by comparing the runoff coefficient, c between two catchments of different characteristics within the Selangor River Basin, namely Catchment A (CA) and Catchment B (CB). Catchment B is a sub-catchment of Catchment A, whereby CA is about 6.8 times larger than to CB. The landscape of CB is largely covered by forest, has higher river slope and a dam completed in year 2004 at upstream of the basin. Annual mean-areal-rainfall (MAR) and total runoff from 1990 until 2018 was used for the rainfall-runoff coefficient, c calculations. The rainfall-runoff response was assessed by separating the analysis into 2-time frames; 1990 to 2000 (before dam construction) and 2007 to 2018 (after dam construction). The analysis before dam construction showed that CB has higher runoff response compared to CA. This was due to the difference in land use, catchment slope and lesser water abstractions compared to CA. The runoff coefficient, c for CA was in the range of 0.19-0.48, while CB was in the range of 0.59-0.79. This means that 19% to 48% of the rainfall was converted into runoff for CA, and 59% to 79% of the precipitation turned into runoff for CB. As suspected, after the dam construction the c coefficient for CB changed to 0.28-0.89, indicating a higher variability in the catchment response due to dam operations and control at the upstream.

Food availability estimation of the blood cockle, Anadara granosa (Linnaeus, 1758), from the aquaculture grounds of the Selangor Coast, Malaysia

Blood cockles, Anadara granosa (Linnaeus, 1758), were collected from the aquaculture grounds (4 stations) of the Selangor coast, Malaysia, and the water quality (water temperature, salinity, dissolved oxygen, turbidity, and chlorophyll a ) was measured from September 2011 to June 2013. At all stations, the water temperature fluctuated around 30℃. At station C, located at the mouth of the Selangor River, the salinity was occasionally lower than 20 PSU. However, the salinity of the other stations fluctuated around 30 PSU. In addition, at all stations, the content of dissolved oxygen generally fluctuated around 3 mg.L -1 or above, and the turbidity changed irregularly, sometimes exceeding 300 Forumajin Turbidity Units. The chlorophyll a content fluctuated mainly ranging 4-20 µg.L -1 at all stations, and values above 20 µg.L -1 were occasionally observed. The phytopigment content, a food availability indicator, in the digestive gland tissue of the blood cockles collected from all stations fluctuated ranging 30-770 µg.g -1 . However, there was no proportional correlation between phytopigment content in the digestive gland and chlorophyll a content at all stations. Therefore, even in a high chlorophyll a content (over 20 µg.L -1 ) environment, the accumulated phytopigment in the digestive gland was around 290 µg.g -1 . In general, these results indicated the cockles were eating a sufficient amount of foods (organic materials including phytoplankton) all year round during the study period. And, the food availability environment in the aquaculture grounds of the Selangor coast was estimated sufficient to grow the blood cockle.

Application of GIS for water quality monitoring in the aquaculture impacted Rawang sub-basin of the Selangor river, Malaysia

Freshwater aquaculture is a prominent activity in Rawang sub-basin of the Selangor River. Despite the importance good water quality in daily life, there is limited study on the impact of aquaculture activities on water quality. This paper discusses water quality parameter status (pH, dissolved oxygen (DO), ammoniacal nitrogen, turbidity, total suspended solids (TSS), chemical oxygen demand (COD) and biochemical oxygen demand (BOD)) based on Water Quality Index (WQI) in the aquaculture-impacted Rawang sub-basin of Selangor River and develops the Inverse Distance Weighted (IDW) maps showing water quality status by using GIS (ArcGIS 10.2.1 software) in order to identify the potential aquaculture impacted sites. Seven river sampling sites were selected including Guntong (SR1), Guntong’s tributaries (SR2/control), Kuang (SR3 and SR7), Gong (SR4), Buaya (SR5), and Serendah (SR6) using random sampling techniques based on accessibility and proximity to aquaculture farms. Seven water quality parameters were recorded and analysed on a bi-monthly basis. Results revealed that Guntong, Kuang, Buaya and Serendah rivers had moderate water quality, whereas Gong River was significantly polluted. The control river recorded clean water quality status. One-way analysis of variance (ANOVA) showed that there were significant differences in all measured water quality parameters among sampling sites (P<0.05).

Verifying the applicability of SWAT to simulate fecal contamination for watershed management of Selangor River, Malaysia

The Soil and Water Assessment Tool (SWAT) ecohydrological model was utilized to simulate fecal contamination in the 1937 km2 Selangor River Watershed in Malaysia. The watershed conditions posed considerable challenges owing to data scarcity and tropical climate conditions, which are very different from the original conditions that SWAT was developed and tested for. Insufficient data were compensated by publicly available data (e.g., land cover, soil, and weather) to run SWAT. In addition, field monitoring and interviews clarified representative situations of pollution sources and loads, which were used as input for the model. Model parameters determined by empirical analyses in the USA (e.g., surface runoff, evapotranspiration, and temperature adjustment for bacteria die-off) are thoroughly discussed. In particular, due consideration was given to tropical climate characteristics such as intense rainfall, high potential evapotranspiration, and high temperatures throughout the year. As a result, the developed SWAT successfully simulated fecal contamination ranging several orders of magnitude along with its spatial distribution (i.e., Nash-Sutcliffe Efficiency (NSE) = 0.64, Root Mean Square Error–Observations Standard Deviation Ratio (RSR) = 0.64 at six mainstem sites, and NSE = 0.67 and RSR = 0.57 at 12 major tributaries). Moreover, mitigation countermeasures for future worsening of fecal contamination (i.e., E.coli concentration > 20,000 CFU/100 mL for 690 days during nine years at a raw water intake point for Kuala Lumpur [KL] residents) were analyzed through scenario simulations, thereby contributing to discussing effective watershed management. The results propose improving decentralized sewage treatment systems and treating chicken manure with effective microorganisms in order to guarantee water safety for KL residents (i.e., E.coli concentrations <20,000 CFU/100 mL throughout the period, considering Malaysian standards). Accordingly, this study verified the applicability of SWAT to simulate fecal contamination in areas that are difficult to model and suggests solutions for watershed management based on quantitative evidence.

Application of Ecological Indices using Macroinvertebrate Assemblages in Relation to Aquaculture Activities in Rawang Sub-basin, Selangor River, Malaysia

This study aimed to evaluate benthic macroinvertebrates assemblages using the application of ecological indices in the Rawang sub-basin of the Selangor River with reference to determining the impacts of aquaculture practices on benthic macroinvertebrates. In total, seven sampling sites were selected, namely Guntong (SR1), Guntong’s tributaries (SR2, control site), Kuang (SR3 and SR7), Gong (SR4), Buaya (SR5) and Serendah (SR6) Rivers. Sites were selected using a random sampling technique and the accessibility and proximity to aquaculture farms. Macroinvertebrates were sampled from April 2019 to March 2020 using an aquatic kick net, a hand spade, and a D-frame dip net. Water samples were also taken. Twenty-seven families belonging to the Annelida, Mollusca and Athropoda phyla were identified using taxonomic keys. Tubificidae was found to be the most dominant family (36.79%) followed by Chironomidae (28.84%). The highest number of individuals from family Tubificidae was recorded in Gong River, where effluent was discharged from an aquaculture farm. The total abundance of benthic macroinvertebrates increased with increased proximity to the aquaculture farm. Based on Biological Monitoring Working Party score value, the water quality of sampling sites in close proximity to the aquaculture farm was polluted as compared to the control location. Family Biotic Index score showed that all sites were substantially polluted with organic pollution, with the control site being much less polluted. In conclusion, benthic macroinvertebrates assemblage was affected by effluent originating from aquaculture farm outlets. It is therefore necessary to conduct aquaculture farming using methods that are deemed to be environmentally friendly.

Comparison among different ASEAN water quality indices for the assessment of the spatial variation of surface water quality in the Selangor river basin, Malaysia.

The assessment of surface water quality is often laborious, expensive and tedious, as well as impractical, especially for the developing and middle-income countries in the ASEAN region. The application of the water quality index (WQI), which depends on several independent key parameters, has great potential and is a useful tool in this region. Therefore, this study aims to find out the spatial variability of various water quality parameters in geographical information system (GIS) environment and perform a comparative study among the ASEAN WQI systems. At present, there are four ASEAN countries which have implemented the WQI system to evaluate their surface water quality, which are (i) Own WQI system-Malaysia, Thailand and Vietnam-and (ii) Adopted WQI system: Indonesia. A spatial distribution of 12 water quality parameters in the Selangor river basin, Malaysia, was plotted and then applied into the different ASEAN WQI systems. The WQI values obtained from the different WQI systems have an appreciable difference, even for the same water samples due to the disparity in the parameter selection and the standards among them. WQI systems which consider all biophysicochemical parameters provide a consistent evaluation (Very Poor), but the system which either considers physicochemical or biochemical parameters gives a relatively lenient evaluation (Fair-Poor). The Selangor river basin is stressed and impacted by all physical, biological and chemical parameters caused by both the aridity of the climate and anthropogenic activities. Therefore, it is crucial to include all these aspects into the evaluation and corresponding actions should be taken.

Prediction of dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand using hydrometeorological variables: case study of Selangor River, Malaysia

In this research, three water quality (WQ) indexes, namely dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD), in Selangor River of peninsular Malaysia were simulated using a stochastic model based on vector auto-regression (VAR). The simulation was adopted based on three modeling scenarios of inputs as predictor: (i) related WQ parameters, (ii) WQ parameters and river flow data, and (iii) WQ parameters and rainfall data. The WQ parameters as input were determined based on the correlation analysis. The numerical analyses revealed that the prediction accuracy of VAR model substantially increases with the increase in input number. The model provided better accuracy in predictions of WQ indexes (root mean square error $$\approx$$ 0.11 and mean absolute error $$\approx$$ 0.26) when all environmental, hydrological, and climatological variables were considered. Further improvement in model performance (root mean square error $$\approx$$ 0.0248 and mean absolute error $$\approx$$ 0.1259) can be achieved if physiochemical parameters like suspended solid material and the turbidity are used as additional inputs.

Integrating cellular automata Markov model to simulate future land use change of a tropical basin.

Predicting land use change is an indispensable aspect in identifying the best development and management of land resources and their potential. This study used certified land-use maps of 1997, 2006, and 2015 combined with ancillary data such as road networks, water bodies and slopes, obtained from the Department of Agriculture and the Department of Surveying and Mapping in Malaysia, respectively. The prediction of future land use changes in the Selangor River basin in Malaysia was performed using the Cellular Automata Markov model. The transition probability matrices were computed using the land use conditions of the periods 1997-2006, 2006-2015, 1997-2015. The performance of the model was very good in its overall ability to simulate the actual land use map of 2015, with the index values of 0.92% and 0.97%, respectively for Kappa for no information and Kappa for grid-cell level location which indicated the reliability of the model to successfully simulate land use changes in 2024 and 2033. Based on the expected results, the future urban area will grow faster (33%) over the next two decades, leading to a decline in forest area that is expected to lose 8% of its total space during these periods. Agricultural land will increase to 4%, while water bodies will change slightly increasing to 1%, and other areas of land use will likely become reservoirs of water, topsoil or new green spaces shrinking at 30%. Given the importance of knowledge of future land use in addressing the problems of uncontrolled development on environmental quality, this study could be valuable for land use planners of the river basin largely covered by natural forest. The study however, suggests future research to integrate geospatial techniques with biophysical and socio-economic factors in simulating land use trends.

Economic and efficiency based optimisation of water quality monitoring network for land use impact assessment

The evaluation of the importance of having accurate and representative stations in a network for river water quality monitoring is always a matter of concern. The minimal budget and time demands of water quality monitoring programme may appear very attractive, especially when dealing with large-scale river watersheds. This article proposes an improved methodology for optimising water quality monitoring network for present and forthcoming monitoring of water quality under a case study of the Selangor River watershed in Malaysia, where different monitoring networks are being used by water management authorities. Knowing that the lack of financial resources in developing countries like Malaysia is one of the reasons for inadequate monitoring network density, to identify an optimised network for cost-efficiency benefits in this study, a geo-statistical technique coupled Kendall's W was first applied to analyse the performance of each monitoring station in the existing networks under the monitored water quality parameters. Second, the present and future changes in non-point pollution sources were simulated using the integrated Cellular Automata and Markov chain model (CA–Markov). Third, Station Potential Pollution Score (SPPS) determined based on Analytic Hierarchy Process (AHP) was used to weight each station under the changes of non-point pollution sources for 2015, 2024, and 2033 prior to prioritisation. Finally, according to the Kendall's W test on kriging results, the weights of non-point sources from the AHP evaluation and fuzzy membership functions, six most efficient sampling stations were identified to build a robust network for the present and future monitoring of water quality status in the Selangor River watershed. This study proposes a useful approach to the pertinent agencies and management authority concerned to establish appropriate methods for developing an efficient water quality monitoring network for tropical rivers.

Variations of water quality in the monitoring network of a tropical river

Rapid development and population growth have resulted in an ever-increasing level of water pollution in Malaysia. Therefore, this study was conducted to assess water quality of Selangor River in Malaysia. The data collected under the river water quality monitoring program by the Department of environment from 2005 to 2015 were used for statistical analyses. The local water quality indices were computed and a trend detection technique and cluster analysis were applied, respectively, to detect changes and spatial disparity in water quality trends. The results showed that the river water is of good quality at all stations, with the exception of 1SR01 and 1SR09 located upstream, which recorded moderate water quality indices of 68 and 71, respectively. The results of trend analysis showed downward trends in dissolved oxygen, biochemical oxygen demand and ammonia nitrogen, for most water quality stations, as well as increasing trends in chemical oxygen, suspended solids, pH and temperature for most stations. In addition, the results of cluster and time series analyses showed that the trend variation in dissolved oxygen, pH, and temperature between the station clusters is relatively low as compared to chemical oxygen demand, biochemical oxygen demand, suspended solids, and ammonia nitrogen. With the peak concentration of 13 mg/L for dissolved oxygen observed in cluster 2 in 2014, and the highest decrease in suspended solids (8 mg/L) observed in cluster 1 for 2015. This finding demonstrates that these combined statistical analyses can be a useful approach for assessing water quality for adequate management of water resources.

Heavy metals in mangrove sediments along the Selangor River, Malaysia

Mangroves are woody plants that grow at the interface between land and sea in tropical and subtropical latitudes where they exist in conditions of high salinity, extreme tides, strong winds, high temperature, and muddy anaerobic soils. The objectives of this study were to determine the selected heavy metals Copper (Cu), Zinc (Zn) and Lead (Pb) contamination in mangrove sediments at the Selangor River, Kampung Kuantan, Kuala Selangor, Selangor, Malaysia; and to compare heavy metals content in mangrove sediments between different plots and different sediment depths. Physical properties (sediment texture and sediment moisture) and chemical properties (pH water, electrical conductivity, and selected heavy metals) of sediments were determined by different plots and depths. The element of Pb was analyzed using the Inductively Coupled Plasma (ICP), whereas Cu and Zn using the Atomic Absorption Spectrometer (AAS). Data obtained were analyzed using the Statistical Analysis System (SAS) version 9.4 software. The results showed that the sediment texture was in the class of sandy clay, and soil moisture in all plots and at all depths were high. The contamination of sediment is affected by many factors, including soil pH and soil electrical conductivity. Cu, Zn and PB in sediment were determined around 1.00-10.60 mg/kg, 215.40-259.00 mg/kg and 18.83-28.59 mg/kg respectively, and were found to experience a significant difference between the plots, but not a significant difference between depths. The sediment in all plots and at all depths was contaminated with these heavy metals because of it being surrounded by residential and industrial areas, combined with particular recreational activities, agriculture and fishing along the Selangor River.

Mitochondrial DNA diversity and gene flow in Southeast Asian populations of the synchronously flashing firefly, Pteroptyx tener Olivier (Coleoptera: Lampyridae)

ABSTRACTPopulations of the iconic, synchronously flashing firefly, Pteroptyx tener (Coleoptera: Lampyridae), are increasingly being threatened by habitat destruction and land conversion activities in Malaysia. Concerns on how this may have impacted the species led us to undertake an analysis of P. tener genetic diversity here. We also investigated how P. tener arrived at its present distribution along the west coast of Peninsular Malaysia using maps of Pleistocene sea levels in Southeast Asia and information on its mitochondrial DNA haplotype distribution. We detected three genetically-distinct populations in Malaysia, one each on the east and west coasts of Peninsular Malaysia and the other in Sarawak/Borneo. We found strong support for a scenario where an ancestral P. tener population diverged to form the Sepetang and Selangor River firefly populations when it became disconnected from the Malacca River system as sea levels rose during the late Pleistocene. An introduction event subsequently occurred when the Selangor and Sepetang River populations contributed to the formation of the Linggi River population. Lastly, the admixture event leading to the founding of the Linggi River population most likely occurred when it was connected to the Malacca River system during periods of changing sea levels in the late Pleistocene.

Streambank Erosion Prediction for Natural Channel using Artificial Neural Network Autoregressive Exogenous (ANNARX) Model

This study aims to develop a streambank erosion prediction model using Artificial Neural Network Autoregressive Exogenous (ANNARX) for natural channels. ANNARX is one type of ANN models and it is a supervised network that trains spasmodic data sets. Field data of 494 data extracted from two (2) rivers in Selangor, namely Sg. Bernam and Sg. Lui were used in the training and testing phases. Total of eleven (11) independent variables are used as input variables in the input layer and the ratio between erosion rates, ? to the near-bank velocity, Ub as the output variable. The functional relationships were derived using Buckingham Pi Theorem in the dimensional analysis. A supervised learning technique was employed and the target output is streambank erosion rates, ?b. The established models were validated to assess their performances in predicting the rates of streambank erosion using 176 data. Validation of the newly developed streambank erosion rates equation has been conducted using data obtained from this study. The performance of the derived model was tested using discrepancy ratio and graphical analysis. Discrepancy ratio (DR) is the ratio of predicted values to the measured values and these values are deemed accurate if the data lie between 0.5 to 2.0 limit. Total of 8 models have been developed in the predictive model. Analysis confirmed that models developed using ANNARX are capable to achieve coefficient correlations (r-squared) values above 0.9 and successfully predict the measured data at accuracy above 90%.

The Relation of Neighbouring Land Use Type and Sedimentary Characteristics of Fluvial Sediments in Tropical Rivers

This study investigates the relationship between land use types and sedimentary characteristics of fluvial bed at three rivers in Selangor, Malaysia; namely Sungai Tengi, Selangor and Langat. The diversity in population density and land development at the adjacent river banks gives associated dominant land use type as forest, agriculture and urban for Tengi, Selangor and Langat, respectively. Field sampling was conducted at consistent spatial locations for Tengi and Selangor, whereas for Langat, the bed sediment was taken at non-uniform spatial points due to limited access. Statistical analysis of the sediment bed mixture shows that forest dominated land gives finer bed sediment size at 650 μm. Correlation analysis proved that there is a relationship between types of land use with the mean sediment size. No distinctive correlation can be observed for sorting, skewness and kurtosis of the sediment with the types of land use.