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This paper aims to develop a soil loss equation used within an equatorial climate (2,300 – 6,000 mm of annual rainfall) and hilly terrain based on measured data from the experiment concerning measured erosion outcomes, called Equatorial Soil Loss Equation (EQSLE). The equation under development should consider high equatorial rainfall intensities and challenging topographical conditions, thus introducing the new factors – Equatorial Rainfall Erosivity Factor (Reqt-factor) and Equatorial Slope Length Factor (Leqt-factor). The process included a photogrammetric method and image processing for raindrop size distribution and an experimental plot setup for a length differential study. The final equation has the form of A=Reqt⸱Leqt⸱S⸱K⸱C⸱P for soil erosion estimation in the equatorial region with the involvement slope and hilly terrain. The performance of the Reqt-factor and Leqt-factor are evaluated using the experimental data, and works of literature are carried out for the results. The formation of the two factors has addressed the challenges of high rainfall intensities and extreme terrain conditions faced by Peninsular Malaysia and East Malaysia. In future work, more case studies on the performance of soil loss rate prediction should be conducted to improve the overall performance of EQSLE as well as the respective factors.

The Bengoh Dam was constructed to secure reliable raw water supply to Kuching and its surrounding areas until 2030. However, the changes in river morphology along the Sarawak Kiri River after the Bengoh Dam construction remain unknown. This research was conducted to study the impact of Bengoh Dam construction on erosion and sedimentation along the Sarawak Kiri River using InfoWorks River Simulation (RS). The novelty is to identify and predict the short- and long-term geomorphology changes along the Sarawak Kiri River over the course of a year. Two scenarios were created, namely, a) with Bengoh Dam b) without Bengoh dam. The model with the Bengoh Dam scenario will be further investigated for ten years of simulation. The five locations selected for comparison and discussion are Bengoh Dam, Bengoh, Danu, Git Village and Batu Kitang. After the dam was built, sediment transport would be hindered, resulting in riverbed alterations. These changes are influenced by the location and proximity to the Bengoh Dam. Simulation results revealed that a huge amount of sediment will be accumulated behind Bengoh Dam. Both scenarios showed that there would be erosion at Bengoh, immediately after the Bengoh Dam, due to a phenomenon known as “Hungry Water.” It is novel to discover that the sedimentation rate at Danu, Git and Batu Kitang, which are located downstream of the Bengoh Dam, has drastically decreased. The capacity of the Batu Kitang Submersible Weir (BKSW) reservoir is unaffected by the constructed Bengoh Dam; hence no dredging must ensure the security of Kuching’s water supply.

Batu Kitang Submersible Weir (BKSW) was constructed to secure reliable water yield from the Sarawak Kiri River until 2010. However, ever-increasing water demand had outstretched the weir capacity. Upgrading work has been done to extend the weir height from 1.5 m to 2.5 m using interlocking concrete blocks to increase water storage capacity. However, the storage capacity and flood extend are unknown after the upgrading work. This study aimed to determine the effect of upgrading works on water storage capacity and floodplain coverage in the upper catchment using InfoWorks River Simulation (RS). Results revealed that weir height extension from 1.5 m to 2.5 m at BKSW had increased the water storage capacity from 1012 ML to 1499.62 ML, securing approximately 48% more water. Besides, maximum storage depth was also increased from 6.53 m to 7.53 m, and the distance of storage reservoir coverage from BKSW also increased from 8 Km to 16 Km. However, these increments would not lead to any significant impact on floodplain coverage upstream. It is novel to discover that a 1m weir height increase has extended BKSW service from 2010 to 2030 under a 1:50-year drought scenario. After 2030, various measures such as inter-basin raw water transfer should be implemented to increase the raw water supply and reduce water usage to ensure a sustainable water supply for Kuching City and its surrounding areas.

Samarahan has transformed from a small village into education hub for the past 2 decades. Rapid development and population growth had led to speedy growth in water demand. The situation is getting worse as the pipes are deteriorating due to pipe aging. Therefore, there is a need to study the adequacy of water supply and relationships among roughness coefficient (C) values in Hazen Williams’ Equation with head loss and water pressure due to pipe aging at Uni-Central, a residential area located at Samarahan Sarawak. Investigations were carried out with Ductile Iron, Abestos Cement and Cast Iron pipes at age categories of 0 - 10 years, 10 - 30 years, 30 - 50 years, 50 - 70 years and >70 years. Six critical nodes named as A, B, C, D, E and F were identified to study the water pressure and head loss. Model was developed with InfoWorks Water Supply (WS) Pro software. The impact of pipe aging and materials to water pressure and head loss was not significant at Nodes A, B, C and F. However, max water pressure at Nodes D and F were only reaching 6.30 m and 7.30 m, respectively for all investigations. Therefore, some improvement works are required. Results also show that Asbestos Cement pipe has the least impact on the head loss and water pressure, followed by Ductile Iron pipe and lastly Cast Iron pipe. Simulation results also revealed that older pipes have higher roughness coefficients, indicated with lower “C” values, thus increase the head loss and reduce the water pressure. In contrast, as “C” values increased, head loss will be reduced and water pressure will be increased.

Coastal erosion along Sarawak shoreline is getting serious in recent years due to the impact of climate change and global warming. Strong waves had washed away the land along the shore or beach and thus threatens the infrastructures that were constructed along Sarawak shoreline. Department of Irrigation and Drainage had adopted many methods to protect the shoreline including seawalls, breakwaters, geotextile tubes, concrete mass Labuan blocks, amour rocks and etc. The cheapest and most economic protection method is using armour rock. Besides, amour rock was found to be strong, durable, long-lasting, able to absorb and dissipate the waves before hitting on the shore. However, appropriate size of amour rocks to protect the shore is unknown. Therefore, this research is carried out to determine the appropriate armour rocks sizes for protecting the shoreline against the waves along Sarawak shoreline. The design methodologies are including determination of tidal levels, design wind speed, wind set-up, wave period, sea level rise, wave set-up, breaker height and lastly design armour rock sizes range. Shoreline Protection Manual and Hudson’s formula were adopted in this research to determine the appropriate sizes range of armour rocks. The study area was subdivided into 7 regions. Results revealed that southern part of Sarawak shorelines requires larger armour rock sizes compared to the northern region. The nominal size ranges of armour rocks for regions 1 and 2 are found to be 2112 mm, 1734 mm for region 3, and 1173 mm for regions 4, 5, 6 and 7. Results also revealed that the required armour rock sizes increase with the increment of Mean High Water Spring, nearshore slope gradients and structure slope. Besides, results also concluded that smaller amours rocks is sufficient to react against smaller waves, while bigger waves required bigger rocks for protecting the shoreline.

This paper proposed the application of Bayesian Principal Component Analysis (BPCA) algorithm to address the issue of missing rainfall data in Kuching City. The experiment was conducted using six different combinations of rainfall data from different neighbouring rainfall stations at different missing data entries (1%, 5%, 10%, 15%, 20%, 25% and 30% of missing data entries). The performance of BPCA model in reconstructing the missing data was examined with respect to Bias (Bs), Efficiency (E) and Root Mean Square Error (RMSE). The reliability and robustness of BPCA was confirmed by comparing its performance with K-Nearest Neighbour (KNN) imputation model. The results support the addition of data from neighbouring rainfall stations to improve the imputation accuracy.

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel input structure for the missing data imputation method. Principal component analysis (PCA) is used to extract the most relevant features from the meteorological data. This paper introduces the combined input of the significant principal components (PCs) and rainfall data from nearest neighbor gauging stations as the input to the estimation of the missing values. Second, the effects of the combination input for infilling the missing rainfall data series were compared using the sine cosine algorithm neural network (SCANN) and feedforward neural network (FFNN). The results showed that SCANN outperformed FFNN imputation in terms of mean absolute error (MAE), root means square error (RMSE) and correlation coefficient (R), with an average accuracy of more than 90%. This study revealed that as the percentage of missingness increased, the precision of both imputation methods reduced.

Identifying the demographic factors that affect patterns is very important for determining drinking water sources within a community. Previous empirical studies mainly focussed on demographic factors affecting water demand in urban and semi-urban areas facing water shortage with little rainfall. However, studies on water consumption in tropical regions (average annual ∼4,000 mm) is limited. This study aims to investigate relationships between drinking water patterns and demographic factors for Kuching community, Sarawak, Malaysia. Survey data were collected from 100 respondents using structured questionnaires through mail, drop-off and telephone surveys in March–December 2016. Results revealed that drinking water patterns are significantly correlated with source, income and education level. 46% of respondents preferred tap water as their drinking water source, followed by 31% for in-home water-treatment devices, 19% chose bottled water and only 4% still drink raw water. In terms of income and education level, 25% of respondents with diploma, degree and postgraduate academic qualifications, quantified as higher income group, installed in-home water-treatment devices. 21% of respondents with monthly income more than RM2500 also initiated installation of in-home water-treatment devices. In contrast, age, gender and religion were found to have little affect on drinking-water consumption patterns.

This paper presents a novel metaheuristic artificial neural network (ANN) model, named as Bat optimisation neural network (BatNN), for spatial downscaling of long-term precipitation. This novel BatNN was developed due to the inefficiency of traditional ANNs in spatial downscaling of large-scale outputs from climate models. Input data are predictors from three climate models including HadCM3, ECHAM5 and HadGEM3-RA combined with observed precipitation collected from Kuching airport rainfall station. The output is the forecasted precipitation. Data from 1961 to 1990 were used for model training, while data from 1991 to 2010 were used for validation. Square root of correlation of determination (r), root mean square error (RMSE), mean absolute error (MAE) and Nash–Sutcliffe coefficient (E) are used to evaluate the models' performance. Results showed that through global and local searches, BatNN is able to avoid local optima trappings. The average r, RMSE, MAE and E for three climate models were yielded to 0.96, 1.69, 1.40 and 0.84, respectively. This reveals that BatNN is able to optimise and forecast long-term precipitation accurately.

With anticipation of global warming and climate change, quantitative prediction of future precipitation trend is more important than ever. Global circulation models (GCMs) are widely used as the base for simulating climate change. However, due to their coarse resolution, researchers have been using various downscaling techniques to produce finer model for regional use. Recent advancements in metaheuristic algorithms have provided an alternative approach in downscaling. This paper introduces the application of a novel optimization algorithm, named as Cuckoo Search Optimization (CSO), to train Feedforward and Recurrent neural network to forecast long term precipitation. As benchmark, CSO was compared with Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) methods. The models were evaluated through validation with historical precipitation; as well as their performance in Pearson correlation (r), root mean square error (RMSE), mean absolute error (MAE), and mean bias (MB). Results showed that CSO is capable of forecasting precipitation up to 90%~100% confidence level with an overall lower mean absolute error, root mean square error and mean bias; outperforming SCG and LM. Future precipitation forecasts revealed that the city will experience an increase of mean annual precipitation by 6~7% over Year 2071-2100. A regional climate model (RCM) with finer resolution was also investigated. Preliminary results revealed an underperformance of the regional climate model due to weaker correlation link between the predictors and historical precipitation.

This paper focuses on the experimental study to determine the relationship of slope length on runoff depth and runoff coefficient. Based on the thorough review of different plot sizes (lengths) for soil loss estimation, the slope of the current experimental plots was designed with an angle of 22.78° from horizontal. The plots were grouped into Plot A (1 m wide x 1 m slope long), Plot B (1 m wide x 2 m slope long) and Plot C (1 m wide x 3 m slope long). Homogenous soil samples were used for all the 3 experimental plots and a tipping bucket rain gauge with automatic data logger was installed at the experimental plot for rainfall intensities collection for the individual rainfall events. Runoff volumes were recorded for the individual rainfall events for determination of runoff coefficients. The rainfall intensities are grouped into ⩽30 mm/h, 30-60 mm/h and ⩾60 mm/h. It was found that the shorter plot (Plot A) has the highest runoff coefficients of 96.34%, 69.89% and 96.26% for 3 different precipitation groups ⩽30 mm/h, 30-60 mm/h and ⩾60 mm/h, respectively. Based on results, the longest plot (Plot C) shows that an increase in mean values of 33.59%, 38.30% and 49.45% as the intensity increased from ⩽30 mm/h to ⩾60 mm/h. From this study, it can be concluded that comparatively longer slope length tends to result in lower runoff depth.

This paper aims to study the natural raindrop size distribution characteristics based on the experimental works for three (3) different rainfall intensities, which are 32.41 mm/h, 56.84 mm/h and 85.73 mm/h. A tipping bucket rain gauge was used to record rainfall data during the field study and a professional DSLR camera (Sony α6000) was used to the capture raindrop distribution during rainfall events. A sufficient number of photographs were taken with necessary adjustments on camera setting to produce the required sharpness of the images and to significantly reduce noise disturbances. Sharp images were selected for image segmentation using a customized coding script to further process the images on MATLAB. The segmented or processed images presented in the form of numerical data would provide the details on the number of drop counts in addition to the diameters of the individual drops captured. The raindrop sizes or diameters ranging from 0.5 mm to 4.5 mm were divided into group of 0.5 mm intervals. It is found that lower rainfall intensity at 32.41 mm/h has the highest volumetric drop distribution of 1.0–1.5 mm interval drop group 78.64%. The relatively higher rainfall intensity at 85.73 mm/h tends to produce higher counts in larger droplet sizes with increments of 6.90% for 2.5–3.0 mm and increment of for 3.45% 4.0–4.5 mm. The study also recorded a significant increment of median droplet sizes (D50) ranging from 1.18 mm to 1.33 mm as the rainfall intensity increased from 32.41 mm/h to 85.73 mm/h, respectively.

Floods are occurring more frequently nowadays. The most common parameter used to analyse the flood severity is average recurrence interval (ARI). Very often, hydrologist found that the severity of flood is 100-year ARI. However, there is a possibility that the 100-year ARI flood severity will hit the same place again after a few months later. The two main possible reasons are (1) future warmer temperatures will increase future extreme precipitation extremes and (2) limited length recorded rainfall data had derived inaccurate Intensity duration frequency (IDF) curves. Therefore, there is an initiative in this study to derive future IDF curve by considering the future rainfall using ‘delta’ approach. The annual maximum precipitations for 2020s, 2050s and 2080s are generated using Statistical Downscaling Model (SDSM), neural network (NN) with scale conjugate gradient and Cuckoo search optimization algorithms. The selected study rainfall station is Kuching Airport. Results revealed that the newly proj...

The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward neural network simulated the current runoff accurately with R = 0.872 and E 2 = 0.775 for the training data set and R = 0.900 and E 2 = 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be to be applied to other basins.

Over the past three decades, Particle Image Velocimetry (PIV), fundamentally based on pattern matching principles, has seen a rapid growth in its usage as a flow measurement technique. PIV is a ‘digital’ tracking process to measure the time taken for water to move from one point to the other. The particle displacements can be ascertained by measuring the movement of the fluid of interest from the images captured by a camera. In this study, PIV is employed to measure surface flow velocity. The flow velocity was obtained by analyzing the series of captured images using MatPIV codes. The experiment was carried out at Swinburne Sarawak Fluid Lab. Digital SLR camera model used is Nikon D40X which is capable of taking 5 frames per second. Various tracer particles were used for the experiments include food dye, beads and ping pong balls. The experiments were conducted in a SOLTEQ Sedimentation Tank. Through the implementation of a standardized correction factor, the corrected PIV velocity has an error margin less than 5% which is deemed to be fairly accurate. The PIV velocity (Vpiv) results showed to be in good agreement with the actual velocity (Vactual).