Klang River Basin Research Articles

Integrated Flood Modeling for Klang River Basin Using HEC-HMS and Radar Rainfall Input

Integrated Flood Modeling for Klang River Basin Using HEC-HMS and Radar Rainfall Input

A Case Study on Assessment and Rehabilitation of IIUM Pusu River for Water Quality Improvement

Pusu river (or Sungai Pusu/Sg. Pusu) is one of the most severely polluted rivers in Malaysia. Pusu river is a tributary of River Gombak in the Klang River Basin, and the IIUM Gombak campus is located at the downstream part of Pusu river. This study was conducted in response to the recent cloudy river flow on the IIUM Gombak campus. Due to the lack of current data for water quality parameters in Pusu river, it is hard to identify the sources that contributed to the river's pollution. Hence, this study was to determine the sources of river water pollution by analysing the parameters for water quality. The analysis of river water quality showed that the average values of pH, conductivity, TDS, TSS, and turbidity were 7.09, 67.6 µs/cm, 46.5 mg/L, 131.8 mg/L, and 717.6 NTU, respectively. It can be seen that Pusu river has a severe sedimentation issue, as turbidity and TSS showed values beyond the allowable limit according to the National Water Quality Standards for Malaysia. As such, several river methods were suggested to rehabilitate Pusu river, which flows through the IIUM Gombak campus.

The impact of sewage treatment plant loading to river basin during Movement Control Order

Environment quality is essential aspects of life on earth, any changes in the quality have a significant impact on human beings. The implementation unprecedented Movement Control Order (MCO), which halted social, economic, and other activities except essential services contributed to opportunities for the earth to rejuvenate itself and build a better blue sky, peaceful wildlife, controlled noise pollution, and improved environmental features. Although the unprecedented Movement Control Order (MCO) due to COVID-19 pandemic posed some adverse effects on the environment, but there are also some benefits on the environment. One of the significant positive effects by the MCO is that the environment had rejuvenated during this period, especially significant water quality improvement. Many reports worldwide, including Malaysia, had reported that the environment including water quality had shown signification improvement. With controlled activities, MCO recognized contributed to river water quality rejuvenation amidst one of the main essential service sewage treatment plant operating at its fullest capacity. Amidst of various river pollution sources, Sewage Treatment Plant (STP) effluent discharge loading was identified as one of the main water resource polluters, and the MCO phenomenon turn raises the question of whether the STP effluent discharge loading onto river resources is the main culprit to the river pollution. This is a new challenge for water resources management to examine the impact of sewage treatment effluent discharge loading on water resource pollution loading in Malaysia. This study investigates the influence of sewerage treatment plant effluent loading from the Kuala Lumpur sewerage catchment to the Klang River basin within the Kuala Lumpur City Centre. The river’s natural self-carrying loading was investigated to evaluate the influence of sewerage effluent loading on the selected river basin. The STPs within the study area were identified and segregated based on its tributary river basin. The individual STP’s Biological Oxygen Demand (BOD) and Suspended Solid (SS) average effluent discharge loading of the year 2020 analyzed against the study River’s BOD and SS average self-loading of the year 2020. The STP and River loading were analyzed to investigate the fraction of STP effluent discharge loading against River loading in the study river basin during MCO.

Short-Term and Long-Term Rainfall Forecasting Using ARIMA Model

Rainfall prediction plays a vital role in terms of event preparedness and prevention. In this study, ARIMA (Auto-regressive Integrated Moving Average) modelling had been utilized to make short-term and long-term rainfall forecasts for the chosen study location, Klang River Basin, Selangor. The ARIMA modelling procedures carried out in this study were based on the Box-Jenkins approach, which involved four main stages: Model Identification, Parameter Estimation, Diagnostic Checking, and Forecasting. Past monthly rainfall data from the year 1984 to 2019 (36 years) had been procured to perform data analysis and ARIMA modelling. Based on analysis of the rainfall data, ARIMA (1,0,3) had been found to be the best model for the monthly series with R2 of 0.78 ,whereas ARIMA (1,0,2) was the best model for the annual series with R2 of 0.52. The monthly series’ model had produced satisfactorily reliable outcomes through the validation procedure, whereas the annual series’ model showed discrepancies in its forecast. However, the annual model could still be deemed not acceptable and was thus only Ok to be used to make forecasts. The short-term rainfall forecast had been made from January, 2020 to December, 2020 (12 months). Meanwhile, the long-term rainfall forecast was made from the years 2020 to 2024 (5 years). Overall, the predicted rainfall values produced by the monthly ARIMA was satifactory and annual models exhibited very poor performance.

Spatial Estimates of Flood Damage and Risk Are Influenced by the Underpinning DEM Resolution: A Case Study in Kuala Lumpur, Malaysia

The sensitivity of simulated flood depth and area to DEM resolution are acknowledged, but their effects on flood damage and risk estimates are less well understood. This study sought to analyse the relative benefits of using global DEMs of different resolution sizes, 5 m AW3D Standard, 12.5 m ALOS PALSAR and 30 m SRTM, to simulate flood inundation, damage and risk. The HEC-RAS 2D model was adopted for flood simulations, and the Toba River in the Klang River Basin in Malaysia was chosen for the case study. Simulated inundation areas from AW3D coincide the most with reported flooded areas, but the coarser-resolution DEMs did capture some of the reported flooded areas. The inundation area increased as the resolution got finer. As a result, AW3D returned almost double flood damage and risk estimates compared to ALOS PALSAR, and almost quadruple compared to SRTM for building-level damage and risk analysis. The findings indicate that a finer-resolution DEM improves inundation modelling and could provide greater flood damage and risk estimates compared to a coarser DEM. However, DEMs of coarser resolution remain useful in data-scarce regions or for large-scale assessments in efforts to manage flood risk.

Water quality assessment and pollution threat to safe water supply for three river basins in Malaysia

Pollution in raw water poses increasing threats to safe water supply in many developing countries. Therefore, a comprehensive water quality assessment is essential to provide various stakeholders the information to deal with this problem. This study applies chemometrics to interpret a recent 10-year water quality data from three major river basins (Selangor River basin, Langat River basin, and Klang River basin) frequented by water supply disruptions in Selangor, Malaysia. We present the application of selected chemometrics approaches, namely agglomerative hierarchical cluster analysis, principal component analysis, factor analysis and Man-Kendall trend analysis. The results showed three spatial groups of monitoring stations with similar land use practices and pollution characteristics. Besides spatial differences, periodic variations were observed when similar pollutants exhibited different pollution loads during rainy and dry periods. We found that nitrogen species, total suspended solids, and dissolved solids represented the major pollution loads in the studied basins. The results further confirmed a significant increasing trend in ammonia pollution. Our study demonstrates how ammonia pollutant is likely to pose a threat to water supply and highlights the vulnerability of Selangor's water resource system to water pollution. The results of this study could facilitate decision making towards more holistic strategies, specifically, incorporating ammonia treatment facilities into the conventional water treatment plant will help achieve smooth water supply operations.

Deep Learning-Based Forecast and Warning of Floods in Klang River, Malaysia

Long short-term memory (LSTM) networks are state of the art technique for time-series sequence learning. They are less commonly applied to the hydrological engineering area especially for river water level time-series data for flood warning and forecasting systems. This paper examines an LSTM network for forecasting the river water level in Klang river basin, Malaysia. The river water level contains of two features dimension and one time-series observed data, in this study, prediction responses for river water level data using a trained recurrent neural network and update the network state function is applied. The radial basis function neural network (RBFNN) in order to get comparison of the generalization solving problem also performed. The performance indicates with the root mean square error, RMSE 0.0253 and coefficient of determination value, R2 0.9815 are closely accurate when updating the network state compared with the RBFNN results. These results verified that the LSTM network with specified training set options is a promising alternative technique to the solution of flood modelling and forecasting problems.

River water level forecasting for flood warning system using deep learning long short-term memory network

Flood is considered chaotic, complex, volatile, and dynamics. Undoubtedly, its prediction is one of the most challenging tasks in time-series forecasting. Long short-term memory (LSTM) networks are a state of the art technique for time-series sequence learning. They are less commonly applied to the hydrological engineering area, especially for river water level time-series data for flood warning and forecasting systems. Yet it is inherently suitable for this subject. This paper examines an LSTM network for forecasting the river water level in Klang river basin, Malaysia. The river water level contains of single time series observed data, with time steps corresponding to hourly data and values corresponding to the water level or stage level in meters. In this study, prediction responses for river water level data using a trained recurrent neural network and update the network state function is applied. The result verified that the LSTM network with specified training set options is a promising alternative technique to the solution of flood modelling and forecasting problems. The performance indicates with the root mean square error, RMSE 0.20593 and coefficient of determination value, R 2 0.844 are closely accurate when updating the network state compared with the observed values.

Modelling of Land-Use/Land-Cover Change and Its Impact On Local Climate of Klang River Basin

Land-use and land-cover (LULC) change has significant effect on local climate variables such as temperature and rainfall as it alters the energy budget, water budget and atmospheric variables. Past studies have shown urban areas can result in high temperature and alter rainfall amounts and intensity. This study therefore looks at spatio-temporal change in land-use/land-cover, and trends in precipitation and temperature for the Klang river basin. The aim of the study is to determine the effects of these changes in land-use on precipitation and temperature trends. Precipitation data was tested using the Mann-Kendall statistics test. Precipitation amount and two precipitation indices (simple day intensity index (SDII) and R95p) for the full year period and both the monsoons and inter-monsoon periods are tested. For temperature, land surface temperature maps of the study area for the years 1999, 2006 and 2017 was created from the thermal band of the images. Remote sensing is used for modelling spatio-temporal changes in LULC of Klang river basin using multi-temporal LandSat dataset (1999, 2006, and 2017). The LULC modelling shows an increase in urban area of 51.5% from 1999-2017, and decrease in natural vegetation 17.6% and cultivated land 35%. The results of precipitation analysis show a mixture of both positive (increasing) and negative (decreasing) trends for stations in the study area, and for temperature there is an increase of 10.1 °C in maximum temperature from 1999-2017. The correlation test shows a positive correlation between precipitation and temperature, but the relationship is not significant. The LULC change has significant impact on temperature; however, the increasing temperature has an insignificant positive correlation with rainfall. This could indicate that the change in rainfall is mostly due to other factors.

Efficient river water quality index prediction considering minimal number of inputs variables

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.

Flood Risk Pattern Recognition Analysis in Klang River Basin

This study was implemented to identify the specific factors that lead to major contribution of floods in Klang River Basin. A thirty-year (1987-2017) database obtained from Department of Irrigation and Drainage (DID), the selected data was analyzed by using integrated Chemometric techniques. The finding from Correlation Analysis revealed strong correlation between stream flow and water level is more than 0.5 (= 0.799). The finding from Principal Component Analysis proved that the selected parameters were significant with the result of R2 > 0.7was applied as a main tool for further analysis. Based on the result, it revealed that stream flow and water level were the most significant hydrological factor that influenced flood risk pattern in Klang River basin. Based on the result from Statistical Process control (SPC), the finding showed that the Upper Control Limit (UCL) for water level was 30.290m. The plotted data which is more than 30.290 m can cause flood to occur in Klang River Basin. Thus, it is very important to continuously monitor and maintain the mitigation measure of flood in the study area to avoid flood to occur. This study also helps to provide visualization of flood pattern and show the optimal rates for the maximum limit for flood control in Klang River Basin.

QUANTITATIVE PRECIPITATION FORECAST USING NUMERICAL WEATHER PREDICTION AND METEOROLOGICAL SATELLITE FOR KELANTAN AND KLANG RIVER BASINS

The unusual heavy rainfall episodes over Kelantan River Basin in 2014 had caused massive destruction and several deaths. The unprecedented storm events at the north-eastern Peninsular Malaysia and many other places indicate the need for enhanced storm forecasting to improve disaster preparedness among the civilian. Quantitative precipitation forecast (QPF) from atmospheric model combined with geostationary meteorological satellite information as input to hydrodynamic model for flood forecasting system can potentially provide improved lead time for warning. In this study, a QPF model is developed using the multilayer neural network with data inputs from the numerical weather prediction (NWP) model products combined with the geostationary meteorological satellite infrared and visible image features to forecast precipitation for a flood-prone area in a tropical region. The results indicate that the model can satisfactorily produce 1-hour rainfall forecast with improved accuracy for larger forecast area. The R2 for areal average rainfall for Kelantan river basin is 0.674 and for Klang river basin is 0.893 whereas the R2 for point rainfall is 0.392 for Kelantan river basin and 0.495 for Klang river basin.

RAINFALL ANALYSIS IN KLANG RIVER BASIN USING CONTINUOUS WAVELET TRANSFORM

The rainfall characteristics within Klang River basin is analyzed by the continuous wavelet transform using monthly rainfall data (1997–2009) from a raingauge and also using daily rainfall data (1998–2013) from the Tropical Rainfall Measuring Mission (TRMM). The wavelet power spectrum showed that some frequency components were presented within the rainfall time series, but the observed time series is short to provide accurate information, thus the daily TRMM rainfall data were used. In such analysis, two main frequency components, i.e., 6 and 12 months, showed to be present during the entire period of 16 years. Such semiannual and annual frequencies were confirmed by the global wavelet power spectra. Finally, the modulation in the 8–16-month and 256–512-day bands were examined by an average of all scales between 8 and 16 months, and 256 and 512 days, respectively, giving a measure of the average monthly/daily variance versus time, where the periods with low or high variance could be identified.

RAINFALL ANALYSIS IN KLANG RIVER BASIN USING CONTINUOUS WAVELET TRANSFORM

The rainfall characteristics within Klang River basin is analyzed by the continuous wavelet transform using monthly rainfall data (1997–2009) from a raingauge and also using daily rainfall data (1998–2013) from the Tropical Rainfall Measuring Mission (TRMM). The wavelet power spectrum showed that some frequency components were presented within the rainfall time series, but the observed time series is short to provide accurate information, thus the daily TRMM rainfall data were used. In such analysis, two main frequency components, i.e., 6 and 12 months, showed to be present during the entire period of 16 years. Such semiannual and annual frequencies were confirmed by the global wavelet power spectra. Finally, the modulation in the 8–16-month and 256–512-day bands were examined by an average of all scales between 8 and 16 months, and 256 and 512 days, respectively, giving a measure of the average monthly/daily variance versus time, where the periods with low or high variance could be identified.

Hydrology Analysis and Modelling for Klang River Basin Flood Hazard Map

Flooding, a common environmental hazard worldwide has in recent times, increased as a result of climate change and urbanization with the effects felt more in developing countries. As a result, the explosive of flooding to Tenaga Nasional Berhad (TNB) substation is increased rapidly due to existing substations are located in flood prone area. By understanding the impact of flood to their substation, TNB has provided the non-structure mitigation with the integration of Flood Hazard Map with their substation. Hydrology analysis is the important part in providing runoff as the input for the hydraulic part.

Assessment of future water availability under the changing climate: case study of Klang River Basin, Malaysia

ABSTRACTThis study aims to assess future water deficit and availability in Klang valley in the context of climate change. A surface water budget model for Klang river basin was developed using MIKE BASIN. The parameters for the Nedbør‐Afrstrømnings (NAM) model were determined and examined by trial-and-error approach and evaluated based on correlation coefficient (R), Nash–Sutcliffe efficiency (RNS), and overall water balance error (WBer). The climate change scenario assessment used downscaled projected future rainfall data from 18 Global Circulation Models for the period 2046–2065. The assessment of water availability was carried out for Klang Gates Dam. The calibration and validation results for NAM model were satisfactory. The reservoir model verification result showed that the observed and simulated dam level were slightly dissimilar, which may be due to insufficient input data, particularly rainfall data as rainfall stations were very sparse upstream of the dam. The water deficit analysis for 2046–2065 showed that there will be water shortage in most of the months. The results of this assessment will help decision-makers evaluate the water availability and existing water infrastructure capacity in the vicinity of Klang river basin, particularly with respect to future raw and treated water yield.

MULTIPLE IMPUTATION FOR HYDROLOGICAL MISSING DATA BY USING A REGRESSION METHOD (KLANG RIVER BASIN)

Rainfall amounts and water surface elevation are considered as one of the most important climatic parameters. Because these two parameters will have a direct impact on water resources management decisions such as meet the water needs and prevent flooding. But in some cases, for some reason all time series data are not fully recorded. To fill the gaps in the data, several interpolation methods currently used. One of these methods is regression analysis as a statistical method. By using regression, we can determine the mathematical relationship coefficients between inputs and outputs. By achieving the equation, we can obtain the unknown quantities. In this research, the daily data between 2005 to 2015 for 5 Rain-gauge stations and 3 elevation measurement of water surface stations in the Klang River Basin were used. The main goal was to find the missing value of the water level in the mentioned three stations by rainfall and water level data. To evaluate the obtained results, Multiple R, R 2 , and Standard Error were used. The results indicate that the standard error in normalized data was less than the regular data. Multiple r values for the Klang at Taman Sri Muda1, Klang at Jam, Sulaiman, WP and Klang at Emp Genting Klang, WP are 0.35, 0.42 and 0.28, respectively.

Characterization of Water Quality Conditions in the Klang River Basin, Malaysia Using Self Organizing Map and K-means Algorithm

This study aimed to determine the spatiotemporal pattern of the water quality data and identifying the sources of pollution in the Klang River Basin. The self organizing map (SOM) combined with the K-means algorithm arranged the data based on the relationships of 25 variables. The data from 2006 to 2009 for 30 monitoring stations were classified into six clusters. Water pollution in this river basin originated primarily from urban runoff, construction sites, faulty septic systems and industrial activities. The application of machine learning approaches is highly recommended to extract valuable information from the data for a holistic river basin management.

Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia.

This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.

Rainfall data analyzing using moving average (MA) model and wavelet multi-resolution intelligent model for noise evaluation to improve the forecasting accuracy

Rainfall forecasting and approximation of its magnitude have a huge and imperative role in water management and runoff forecasting. The main objective of this paper is to obtain the relationship between rainfall time series achieved from wavelet transform (WT) and moving average (MA) in Klang River basin, Malaysia. For this purpose, the Haar and Dmey WTs were applied to decompose the rainfall time series into 7, 10 different resolution levels, respectively. Several preprocessing case studies based on 2-, 3-, 5-, 10-, 15-, 20-, 25-, and 30-month MAs were carried out to discover a longer-term trend compared to a shorter-term MA. The information and data were gathered from Klang Gates Dam, Malaysia, from 1997 to 2008. Regarding the behavior, the time series of 10-, 15-, 20-, and 30-day rainfall are decomposed into approximation and details coefficient with different kind of WT. Correlation coefficient R 2 and root-mean-square error criteria are applied to examine the performance of the models. The results show that there are some similarities between MA filters and wavelet approximation sub-series filters due to noise elimination. Moreover, the results obtained that the high correlation with MAs can be achieved via Dmey WT compared to Haar wavelet for rainfall data. Moreover, clean signals could be used as model inputs to improve the model performance. Therefore, signal decomposition techniques for the purpose of data preprocessing could be favorable and could be appropriate for elimination of the errors.