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The 1954 Soil Conservation Services (SCS) runoff predictive model was adopted in engineering designs throughout the world. However, its runoff prediction reliability was under scrutiny by recent studies. The conventional curve number (CN) selection methodology is often very subjective and lacks scientific justification while nested soil group catchments complicate the issue with the risk of inappropriate curve number selection which produces unreliable runoff results. The SCS CN model was statistically invalid ( $\alpha = 0.01$ level) and over predicted runoff volume as much as 21% at the Sungai Kerayong catchment in Kuala Lumpur, Malaysia. Blind adoption of the model will commit a type II error. As such, this study presented a new method to calibrate and formulate an urban runoff model with inferential statistics and residual modelling technique to correct the runoff prediction results from the SCS CN model with a corrected equation. The new model out-performed the Asymptotic runoff model and SCS CN runoff model with low predictive model bias, reduced sum of squared errors by 32% and achieved high Nash-Sutcliffe efficiency value of 0.96. The derived urban curve number is 98.0 with 99% confidence interval ranging from 97.8 to 99.5 for Sungai Kerayong catchment. Twenty-five storms generated almost 29 million $m^{3}$ runoff (11,548 Olympic size swimming pools) from the Sungai Kerayong catchment in this study. 75%-94% of the rain water became runoff from those storms and lost through the catchment, without efficient drainage infrastructure in place, the averaged flood depth reached 6.5 cm while the actual flood depth will be deeper at the flood ponding area near to the catchment outlet.

Sedimentation management is one of the primary factors in achieving sustainable development of water resources. However, due to difficulties in conducting in-situ tests, and the complex nature of fine sediments, it remains a challenging task when dealing with issues related to settling velocity. Hence, the machine learning model appears as a suitable tool to predict the settling velocity of fine sediments in water bodies. In this study, three different machine learning-based models, namely, the radial basis function neural network (RBFNN), back propagation neural network (BPNN), and self-organizing feature map (SOFM), were developed with four hydraulic parameters, including the inlet depth, particle size, and the relative x and y particle positions. The five distinct statistical measures, consisting of the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), mean value accounted for (MVAF), and total variance explained (TVE), were used to assess the performance of the models. The SOFM with the 25 × 25 Kohonen map had shown superior results with RMSE of 0.001307, NSE of 0.7170, MAE of 0.000647, MVAF of 101.25%, and TVE of 71.71%.

Oil palm crop yield is sensitive to heat and drought. Therefore, El Niño events affect oil palm production, resulting in price fluctuations of crude palm oil due to global supply shortage. This study developed a new Fresh Fruit Bunch Index (FFBI) model based on the monthly oil palm fresh fruit bunch (FFB) yield data, which correlates directly with the Oceanic Niño Index (ONI) to model the impact of past El Niño events in Malaysia in terms of production and economic losses. FFBI is derived from Malaysian monthly FFB yields from January 1986 to July 2021 in the same way ONI is derived from monthly sea surface temperatures (SST). With FFBI model, the Malaysian oil palm yields are better correlated with ONI and have higher predictive ability. The descriptive and inferential statistical assessments show that the newly proposed FFBI time series model (adjusted R-squared = 0.9312 and residual median = 0.0051) has a better monthly oil palm yield predictive ability than the FFB model (adjusted R-squared = 0.8274 and residual median = 0.0077). The FFBI model also revealed an oil palm under yield concern of the Malaysian oil palm industry in the next thirty-month forecasted period from July 2021 to December 2023.

Extensive green roof is one of the sustainable urban stormwater management alternatives to manage and mitigate the urban surface runoff. In order to implement green roofs more effectively, suitable plant species and substrate components for tropical climate must be identified. The aim of this study is to investigate the evapotranspiration (ET) behaviors in extensive green roofs based on different substrate types and local native plant species. Four green roof test beds containing pro-mixing pot and burn soils were each vegetated with Axonopus Compressus (grass) and Portulaca Grandiflora (sedum). A weather station with soil moisture sensors was installed to measure the weather and soil moisture data. The results showed that the mean ET rates for grass-pot soil, sedum-pot soil, grass-burn soil and sedum-burn soil were 1.32 ± 0.41 mm/day, 2.31 ± 0.72 mm/day, 1.47 ± 0.39 mm/day and 2.31 ± 0.43 mm/day, respectively. It is noted that environmental parameters such as ambient temperature, solar radiation and wind speed showed significantly positive relationship (p value &lt, 0.01) with ET rates of green roofs except relative humidity. The crop coefficients (Ks) for the studied green roof plant species are estimated based on actual and reference evapotranspiration rates. The sedum planted in burn soil showed the highest crop coefficient (0.64), followed by sedum in pot soil (0.62), grass in burn soil (0.39) and grass in pot soils (0.37), respectively. The findings in this study also showed that substrate with better water retention capacity generally improved the Ks values.

History shows that earthquakes from neighbouring countries cause great structural impacts on Malaysia. A tuned mass damper which is a damping system installed at a specific floor of a high-rise building could minimise the acceleration and dissipation of seismic energy from an earthquake. The case study is a high-rise structure scaled at 1:100, built based on an actual building’s tender drawings. The aim of this study is to investigate the dynamic response of scaled structures with one liquid tuned mass damper. There are three types of Tune Liquid Mass Damper (TLMD) being investigated in this research. TLMD of 0 grams (TLMD-0), 337 grams (TLMD-337) and 913 grams (TLMD-913) of liquid were being installed individually on the roof service floor for dynamic load test. The findings show the scaled model with an increase in mass of TLMD caused the increase of roof top displacement by 15.56 % for TLMD-337 and 27.93 % for TLMD-913 compared to TLMD-0. This finding is due to the increase of inertial force generated by TLMD. The breakthrough of this research shows the ground displacements are reduced by 7.94 % for TLMD-337 and 5.81 % for TLMD-913 as compared to TLMD-0. In conclusion, the novelty of this research is the case study building managed to sustain Seismic Intensity until Level VII with 29.69 % of lateral acceleration reduction for TLMD-337 and 37.26 % for TLMD-913 by adopting effective water depth ratio of 0.286 and mass ratio of 0.11.

Experiments were conducted to investigate the seabed scour holes due to the interaction between the twin-propeller jet and quay wall. Vertical quay wall was modelled by using a polyvinyl chloride (PVC) plastic plate in a water tank. The relationship between the positions of the propeller and the vertical quay wall was designed according to the actual working conditions of a ship entering and leaving a port. Propeller-to-wall distance and rotational speed were changed to observe the various scour conditions. The scour depth was measured by using an Acoustic Doppler Velocimeter (ADV). Primary scour hole was found within the jet downstream and secondary scour hole occurred beneath of the propeller. Third scour hole was found close to the quay wall due to horseshoe vortices. The maximum scour position of this third scour hole was found at the jet centre near the quay wall. Temporal formation of scour holes can be divided into three stages: axial scour formation, obstructed scour expansion and equilibrium stages. The quantitative relationships for six characteristic parameters of the scour pit were established including the maximum scour depth (εmax,q), maximum scour depth position (Xm，q), maximum scour width (Wm，q), length of main scour pit (XS，q), maximum deposition height (ZD，q), and location of maximum deposition height (XD，q).

In this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies&rsquo, optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions.

Malaysian government agencies adopted curve number (CN) rainfall-runoff model for design use like many other commercial software applications, while most researchers have adopted CN values from its published handbook from USA. However, there is no regional-specific curve numbers handbook in Malaysia for the rainfall-runoff predictive modelling. This study did not refer to any CN value but derived a statistically significant CN value with rainfall-runoff events directly. The derived λ = 0.0002 is statistically significant (α = 0.01), while the optimum CN value of 92.95 represents the rainfall-runoff characteristic at the Sungai Kayu Ara catchment. The runoff predictive model estimated an averaged flood depth of 7.46 cm from 100 mm rainfall event when the drainage infrastructure fails to drain away the runoff volume effectively. It is recommended to limit the upstream development, while rainwater harvesting, storm water retention, and detention facilities should be constructed to curb the urban flash flooding at the Sungai Kayu Ara catchment.

Kelantan is a flood-prone area where in the past years flood had occurred quite frequently. Determining a hydrological model that can represent Kelantan River basin by giving plausible simulated runoff according to the observed runoff is essential as this will allow appropriate prediction of future flood by using forecasted rainfall and other data. In this study, the IHACRES model was used to simulate runoff and the calibrated simulated runoff by daily scale and seasonal flood events were compared with observed runoff. In general, the IHACRES model performed better in seasonal scale as compared with annual scale in terms of calibration. However, performance of IHACRES degraded during validation stage, whereby the model tends to underestimate the high peak flows but estimate rather more accurate when no peak flows were present. In terms of annual scale, the best model was obtained by calibrating the streamflow in 2012 – 2013 (2 years), the validation results were not satisfactory with NSE = 0.473 and PBIAS = 27.7%. On the other hand, for seasonal analysis, the best model was obtained by calibrating the data of NEM 6 (November 2017 – March 2018). 3 out of 5 of the validation periods show unsatisfactory results (NSE ≤ 0.50). NEM 1 (November 2012 – March 2013) show the best validation results with NSE = 0.853. Further calibration is required in order to enhance the accuracy of the model.

For the study of water resources of a catchment, an immediate task would be to establish a good model for predicting the probable daily rainfall occurrence and rainfall amount. This study presents the simulation of daily rainfall occurrence using the generalized linear model (GLM), the non-homogeneous hidden Markov model (NHMM) and the bootstrap aggregated classification tree (BACT) model. The major challenge of NHMM is the determination of optimum number of hidden states, which can be achieved using the Bayesian information criterion score. While the determination of number of grown tree is another challenge for BACT model, this critical task can be achieved with the help of out-of-bag classification error. Both the NHMM and BACT model outperformed the GLM to capture the rainfall persistence and spell lengths distribution. Through the validation phase, the BACT model exhibited better performance with the higher indices of probability of detection, critical success index, Heidke skill score and Peirce skill score, than other models. The prediction ability of the NHMM is equivalent to an unskilled random forecast with the skill scores nearly equal to zero. At the end, the BACT model was recommended as the appropriate daily rainfall occurrence model for this study.

Since the inception in 1954, researchers started to scrutinise the United States Department of Agriculture (USDA) Soil Conservation Services (SCS) rainfall runoff model with different field data after the model produced inconsistent runoff prediction results throughout the world. This paper re-assessed two key hypotheses used by SCS where Ia = 0.2S and λ = 0.2 as a constant. The 112 original SCS data points were used to re-determine the correlation between Ia and S with Bootstrapping, BCa procedure. Both key hypotheses of SCS were proven to be statistical in-significant. Inferential statistics deduced that Ia ≠ 0.2S while λ is neither equal to 0.2 nor a constant at alpha = 0.01 level. Both hypotheses are not even applicable to the original dataset used by then SCS to formulate the rainfall runoff model. Ia = 0.112S fitted SCS original data points better at alpha = 0.01 level. The 1954 SCS proposal of Ia = 0.2S and λ = 0.2 committed type II error as pertain to its own dataset. Therefore, SCS rainfall runoff model cannot be blindly adopted. Practitioners of this model are encouraged to validate and derive regional specific relationship between Ia and S.

Flood related disasters continue to threaten mankind despite preventative efforts in technological advancement. Since 1954, the Soil Conservation Services (SCS) Curve Number (CN0.2) rainfall-runoff model has been widely used but reportedly produced inconsistent results in field studies worldwide. As such, this article presents methodology to reassess the validity of the model and perform model calibration with inferential statistics. A closed form equation was solved to narrow previous research gap with a derived 3D runoff difference model for type II error assessment. Under this study, the SCS runoff model is statistically insignificant (alpha = 0.01) without calibration. Curve Number CN0.2 = 72.58 for Peninsula Malaysia with a 99% confidence interval range of 67 to 76. Within these CN0.2 areas, SCS model underpredicts runoff amounts when the rainfall depth of a storm is &lt, 70 mm. Its overprediction tendency worsens in cases involving larger storm events. For areas of 1 km2, it underpredicted runoff amount the most (2.4 million liters) at CN0.2 = 67 and the rainfall depth of 55 mm while it nearly overpredicted runoff amount by 25 million liters when the storm depth reached 430 mm in Peninsula Malaysia. The SCS model must be validated with rainfall-runoff datasets prior to its adoption for runoff prediction in any part of the world. SCS practitioners are encouraged to adopt the general formulae from this article to derive assessment models and equations for their studies.

Flash floods are known as one of the common natural disasters that cost over billions of Ringgit Malaysia throughout history. Academically, an extreme rainfall model is effective in modelling to predict and prevent the occurrence of flash floods. This paper compares four probability distributions, namely, exponential distribution, generalized extreme value distribution, gamma distribution, and Weibull distribution, with the rainfall data of 10 stations in peninsular Malaysia. The period of the data is from 1975 to 2008. The comparison is based on the descriptive and predictive analytics of the models. The determination of the most effective model is through Kolmogorov-Smirnov, Anderson-Darling, and chi-square test. The result shows that generalized extreme value is the most preferred extreme rainfall model for the rainfall cases in Peninsular Malaysia.

The US Department of Agriculture (USDA), Soil Conservation Services (SCS) rainfall-runoff model has been applied worldwide since 1954 and adopted by Malaysian government agencies. Malaysia does not have regional specific curve numbers (CN) available for the use in rainfall-runoff modelling, and therefore a SCS-CN practitioner has no option but to adopt its guideline and handbook values which are specific to the US region. The selection of CN to represent a watershed becomes subjective and even inconsistent to represent similar land cover area. In recent decades, hydrologists argue about the accuracy of the predicted runoff results from the model and challenge the validity of the key parameter, initial abstraction ratio coefficient (λ) and the use of CN. Unlike the conventional SCS-CN technique, the proposed calibration methodology in this chapter discarded the use of CN as input to the SCS model and derived statistically significant CN value of a specific region through rainfall-runoff events directly under the guide of inferential statistics. Between July and October of 2004, the derived λ was 0.015, while λ = 0.20 was rejected at alpha = 0.01 level at Melana watershed in Johor, Malaysia. Optimum CN of 88.9 was derived from the 99% confidence interval range from 87.4 to 96.6 at Melana watershed. Residual sum of square (RSS) was reduced by 79% while the runoff model of Nash–Sutcliffe was improved by 233%. The SCS rainfall-runoff model can be calibrated quickly to address urban runoff prediction challenge under rapid land use and land cover changes.

Since 1954, the Soil Conservation Curve Number (SCS-CN) method is widely applied in hydrological field to predict the direct runoff resulting from event rainfall. Originally, the lambda value was fixed at 0.2. However, based on recent studies, the simplied SCS-CN method was unable to predict a consistent and accurate runoff amount. Most of the research studies in various countries claimed that lambda value was a variable and most likely should be less than 0.2. Most of the researchers applied either mean or median λ value directly without checking the statistics. Misuse of lambda value in SCS-CN model will lead to inconsistent runoff estimation. Moreover, although λ value can be determined, the equivalent CN0.2 cannot be found. Some of research studies or even Hydrology Textbook substitute Sλ directly into CN equation and lead to wrong CN calculation. In this study, the statistical significant λ value and regional specific S correlation are developed under the guide of non-parametric inferential statistics. by using bootstrapping, Bias corrected and accelerated (BCa) technique with 2000 samples.

In the past, theCNwas determined through SCS handbook. In order to determine runoff prediction using SCS-CN model, selection ofCNis important. However, the conventionalCNmethodology with inappropriateCNselection often produces inconsistent runoff estimation. Thus, the new direct curve number derivation technique based on rainfall-runoff datasets with supervised numerical optimization technique under the guide of inferential statistics was developed to improve the accuracy of surface runoff prediction. Furthermore, the two decimal pointCNsystem was proposed in this study. The optimumCNof Melana site is 90.45 at alpha 0.01 with BCa 99 % confidence interval range from 90.45 to 95.12. The regional specific calibrated SCS-CN model with two decimal pointCNderivation technique is out-performed the runoff prediction of conventional SCS-CN model and the asymptotic curve number fitting method.

This study aims to investigate separate and combined impacts of land-use and climate variability on hydrological components in the Johor River Basin (JRB), Malaysia. The Mann-Kendall and Sen’s slope tests were applied to detect the trends in precipitation, temperature and streamflow of the JRB. The Soil and Water Assessment Tool (SWAT) was calibrated and validated using measured monthly streamflow data. The validation results showed that SWAT was reliable in the tropical JRB. The trend analysis showed that there was an insignificant increasing trend for streamflow, whereas significant increasing trends for precipitation and temperature were found. The combined (climate + land-use change) impact caused the annual streamflow and evaporation to increase by 4.4% and 1.2%, respectively. Climate (land-use) raised annual streamflow by 4.4% (0.06%) and evaporation by 2.2% (−0.2%). Climate change imposed a stronger impact than land-use change on the streamflow and evaporation. These findings are useful for...

Malaysian water demand is increasing at an alarming rate reaching 27 to 38% higher than the World Health Organisation recommended consumption limit of 165 liters per capita per day. Therefore, the Malaysian water shortage crisis is quite possible in future due to this water demand uptrend. The average annual rainfall of Malaysia is 2,400 mm but large portion of this fresh water resource becomes runoff and lost through our catchments. Urban flash flood is also becoming more frequent due to fast pace of urban development and anthropogenic induced runoff. Malaysia has experienced drought and flooding in different areas and therefore, it is crucial to study the feasibility of alternate water resources in Malaysia to manage and maintain the sustainability of urban township. This study reviewed a past rain water harvesting system (RWHS) case and conducted the financial payback analyses on its proposed system. If there were 177 rain days per year with at least 52 mm of rainfall event depth, the payback period of the proposed RWHS would be 5.8 years when the discount rate (i%) = 2% and 8.2 years if i% = 10%. The payback period became longer when the annual rain days dropped below 106 and 89 rain days per year. If the proposed RWHS only serve the community under this study, it will take 12 days to consume 800 m3 stored water, while any rainfall of consecutive days will not be harvested as the underground storage tank is in full capacity. The proposed RWHS must be filled up at least 38 times per year in order to break even with the proposed annual maintenance cost but will never be able to achieve any payback from its initial investment. Rain water harvesting and full utilisation is the only way to achieve high water cost savings and shorter payback period, and maximise urban excess runoff reduction.

The Soil Conservation Services (SCS) rainfall-runoff model has been incorporated into many types of software and adopted by Malaysian government agencies for design use. However, hydrologists argue the accuracy of the predicted runoff results from this model in recent decades. Malaysia does not have a regional specific curve numbers available for the use in rainfall-runoff modelling, and therefore SCS-CN practitioner has no option but to adopt its guideline and handbook values which are specific to US region. Unlike the conventional SCS-CN technique, the proposed calibration methodology in this paper discarded the use of CN as input to the SCS model, rearranged the model equation and derived statistically significant CN value(s) of a specific region through rainfall-runoff events directly through the use of inferential statistics. The SCS rainfall-runoff framework can now be calibrated quickly to address urban runoff prediction challenge under rapid land use and land cover or climate changes with the proposed methodology. Between July and October of 2004, the derived λ was 0.0005 while λ = 0.20 was found to be statistically insignificant at alpha = 0.01 level at Melana watershed in Johor, Malaysia. Optimum CN of 90.45 was derived to represent the rainfall-runoff characteristic of Melana watershed. Runoff prediction error was reduced by 90% and achieved the Nash-Sutcliffe (NS) index of 0.86. The new method can provide CN adjustment guidelines for SCS practitioners and any software which incorporated the model to predict urban runoff.

An accurate streamflow forecasting model is important for the development of flood mitigation plan as to ensure sustainable development for a river basin. This study adopted Variational Mode Decomposition (VMD) data-preprocessing technique to process and denoise the rainfall data before putting into the Support Vector Machine (SVM) streamflow forecasting model in order to improve the performance of the selected model. Rainfall data and river water level data for the period of 1996-2016 were used for this purpose. Homogeneity tests (Standard Normal Homogeneity Test, the Buishand Range Test, the Pettitt Test and the Von Neumann Ratio Test) and normality tests (Shapiro-Wilk Test, Anderson-Darling Test, Lilliefors Test and Jarque-Bera Test) had been carried out on the rainfall series. Homogenous and non-normally distributed data were found in all the stations, respectively. From the recorded rainfall data, it was observed that Dungun River Basin possessed higher monthly rainfall from November to February, which was during the Northeast Monsoon. Thus, the monthly and seasonal rainfall series of this monsoon would be the main focus for this research as floods usually happen during the Northeast Monsoon period. The predicted water levels from SVM model were assessed with the observed water level using non-parametric statistical tests (Biased Method, Kendall’s Tau B Test and Spearman’s Rho Test).

Initial abstraction coefficient ratio (λ) within the runoff prediction model proposed by the United States Department of Agriculture (USDA), Soil Conservation Services (SCS) in 1954 produced inconsistent runoff results according to worldwide research findings. SCS proposed a linear correlation between initial abstraction (Ia) and total abstraction (S) where Ia = λS. The proposed correlation by then was re-assessed using non-parametric inferential statistics to deduce a different conclusion in this study. Practitioners are encouraged to validate and employ the runoff prediction model with caution.

Researchers started to cross examine United States Department of Agriculture (USDA) Soil Conservation Services (SCS) Curve Number (CN) methodology after the technique produced inconsistent results throughout the world. More field data from recent decades were leaning against the assumption of the initial abstraction coefficient ratio value proposed by SCS in 1954. Physiographic conditions were identified as vital influencing factors to be considered under this methodology while practitioners of this method are encouraged to validate and derive regional specific relationship and employ the method with caution.