Search

Detecting breast cancer (BC) at the initial stages of progression has always been regarded as a lifesaving intervention. With modern technology, extensive studies have unraveled the complexity of BC, but the current standard practice of early breast cancer screening and clinical management of cancer progression is still heavily dependent on tissue biopsies, which are invasive and limited in capturing definitive cancer signatures for more comprehensive applications to improve outcomes in BC care and treatments. In recent years, reviews and studies have shown that liquid biopsies in the form of blood, containing free circulating and exosomal microRNAs (miRNAs), have become increasingly evident as a potential minimally invasive alternative to tissue biopsy or as a complement to biomarkers in assessing and classifying BC. As such, in this review, the potential of miRNAs as the key BC signatures in liquid biopsy are addressed, including the role of artificial intelligence (AI) and machine learning platforms (ML), in capitalizing on the big data of miRNA for a more comprehensive assessment of the cancer, leading to practical clinical utility in BC management.

This study presents a revised and calibrated Soil Conservation Service (SCS) curve number (CN) rainfall runoff model for predicting runoff in Malaysia using a new power correlation Ia = SL, where L represents the initial abstraction coefficient ratio. The traditional SCS-CN model with the proposed relation Ia = 0.2S is found to be unreliable, and the revised model exhibits improved accuracy. The study emphasizes the need to design flood control infrastructure based on the maximum estimated runoff amount to avoid underestimation of the runoff volume. If the flood control infrastructure is designed based on the optimum CN0.2 values, it could lead to an underestimation of the runoff volume of 50,100 m3 per 1 km2 catchment area in Malaysia. The forest areas reduced by 25% in Peninsular Malaysia from the 1970s to the 1990s and 9% in East Malaysia from the 1980s to the 2010s, which was accompanied by an increase in decadal runoff difference, with the most significant rises of 108% in Peninsular Malaysia from the 1970s to the 1990s and 32% in East Malaysia from the 1980s to the 2010s. This study recommends taking land use changes into account during flood prevention planning to effectively address flood issues. Overall, the findings of this study have significant implications for flood prevention and land use management in Malaysia. The revised model presents a viable alternative to the conventional SCS-CN model, with a focus on estimating the maximum runoff amount and accounting for land use alterations in flood prevention planning. This approach has the potential to enhance flood management in the region.

This study calibrated the Soil Conservation Service Curve Number (SCS-CN) model to predict decadal runoff in Peninsula Malaysia and found a correlation between the reduction of forest area, urbanization, and an increase in runoff volume. The conventional SCS-CN runoff model was found to commit a type II error in this study and must be pre-justified with statistics and calibrated before being adopted for any runoff prediction. Between 1970 and 2000, deforestation in Peninsula Malaysia caused a decline in forested land by 25.5%, resulting in a substantial rise in excess runoff by 10.2%. The inter-decadal mean runoff differences were more pronounced in forested and rural catchments (lower CN classes) compared to urban areas. The study also found that the CN value is a sensitive parameter, and changing it by ±10% can significantly impact the average runoff estimate by 40%. Therefore, SCS practitioners are advised not to adjust the CN value for better runoff modeling results. Additionally, NASA’s Giovanni system was used to generate 20 years of monthly rainfall data from 2001–2020 for trend analysis and short-term rainfall forecasting. However, there was no significant uptrend in rainfall within the period studied, and occurrences of flood and landslide incidents were likely attributed to land-use changes in Peninsula Malaysia.

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.

Ageing oil palm crops show a significant correlation with the declining oil palm yield in Malaysia. Not only do aged crops result in lower production, but they are also more costly and difficult to harvest. The Malaysian oil palm yield recovered to the pre-El Niño level after the 1997/98 El Niño event. However, the oil palm yield failed to recover after the recent 2015/16 El Niño. Due to the accumulation of aged oil palm plantations in Malaysia, the financial losses from different magnitudes of El Niño events are increasing. Thirty-four years of monthly oil palm yield trends in Malaysia were compared with the El Niño–free yield dataset to show that the oil palm yield downtrend pattern is the same with or without El Niño events in Malaysia for the most recent 15 years (2005 to 2019). The performance of oil palm yield did not show any significant difference from 2000 to 2019. This study estimates that ageing oil palms would lead to a minimum opportunity loss of USD 431 million by December 2022. Without a proper replanting program, the total combined loss attributable to the ageing crops from 2009 to 2022 is estimated to be USD 3.94 billion, which is more profound than losses due to El Niño events within the same period. This study also concluded that a continuous 7-year replanting scheme of at least 115,000 hectares per year is needed to address the adverse impact of ageing crops on the Malaysian oil palm yield, which accounts for nearly 30% of the global palm oil production.

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.

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).

Inconsistent model prediction results were reported worldwide against SCS (now USDA) runoff model since its inception in 1954. Non parametric inferential statistics was used to reject two Null hypotheses and guided the numerical analysis optimization study to formulate a statistical significant new runoff prediction model. The technique performed regional hydrological conditions calibration to SCS base runoff model and improved runoff prediction by 27 % compared to the non-calibrated empirical model. A rainfall runoff difference model was created as a collective visual representation of runoff prediction error from the non-calibrated SCS empirical model under multiple rainfall depths and CN scenarios in Peninsula Malaysia. Statistical significant correction equations were formulated through swift data mining from the model to study the under and over-design worse case scenarios which are nearly impossible to quantify by solving the complex mathematical equation. Critical curve number concept was introduced in this study.

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.