Recently, a novel data mining technique, Multivariate Adaptive Regression Splines (MARS) has begun attracted attention from several hydrological researchers because their application is relatively new in modelling hydrological processes. The power of this approach has been proven in variety learning problems such as financial analysis, species distributions modelling, and doweled pavement performance modelling. Therefore, the objective of this paper is to investigate the performance of MARS model in capture the rainfall-runoff processes at river catchment of Malaysia. Pahang River has been selected as area of study. 30-years data set of daily rainfall and runoff at upstream tributaries of Pahang River were used to developed and validate the capability of MARS model in flood prediction. The effect of different length of record data to performance of MARS model was also examined by arranged the data into 5-years data set, 10 years data set, 20 years data set, and 30 years data set. All these data sets used 1-year data of 2003 for validation process while the others were applied for calibration. Simulation results showed that MARS model was able to learn the rainfall-runoff processes in Pahang River catchment and the model performance improved due to the longer period of data.
This paper reviews on the water quality for Carey Island which is one of the mangrove islands in Malaysia. The mangrove area has vital functions of its mangrove tress. In order to control and protect the level of contamination at this area, a study on water quality has been carried out. Three rivers that selected as a point of study are Air Hitam, Judah and Keluang River. The parameters tested for the water quality are Temperature, pH, Dissolved Oxygen (DO), (NH 3 -N). The parameters were tested by in-situ and laboratory testing. Finally, it is found that the Air Hitam, Judah and Keluang rivers are experiencing contamination and pollution problems. Water Quality Index calculation shows that all the rivers fall under Class III River category. Air Hitam River has the worst quality followed by Keluang River and Judah River Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Turbidity, Total Suspended Solid (TSS), Total Organic Carbon (TOC) and Ammoniacal Nitrogen
Sustainable water resources management and social rights for equitable safe water and sanitation are the focal point for long-term economic growth and productivity, health and education. By 2030, the Sustainable Development Goal (SDG) 6 that underlines the imperativeness of Clean Water and Sanitation aims at improving water quality by curtailing the rate and threat of pollution. This idea also aims at convincing the public on recycling water and safe water reuse. Indeed, global warming, water shortages, exponential growth of world's population and water pollution have caused significant impact on availability of the water resource throughout the globe. The trend of recycling wastewater has become one of the alternatives to reduce the pressure on the consumption of water resources. The key implementation for recycled wastewater derives from the intensive work in ensuring recycled wastewater is accepted by the public. Ergo, the challenge of the implementation is to remove the stereotype perception on the quality of recycled wastewater. The objectives of this study are to determine the acceptance of adults and senior adults to utilize recycled wastewater in the form of potable and non-potable water and to determine the correlation between the respondents' willingness in correspond to their age and education background. The survey was participated by the adults and senior adults' residents of Taman Bukit Perdana, Johor Darul Tazim and the data collected was analyzed using IBM SPSS version 27. Observations have found 77% of the respondents are willing to utilize the recycled wastewater. Adults range age between 18 -59 years old are more likely to support the utilization of recycled
In developing countries, data is usually a scarce resource as data collection is an expensive exercise. Therefore, analytical method is required to simulate the actual situations and provide synthetic data for forecasting purposes. This paper will compare several methods of synthetically generating rainfall data based on available data. Several models will be used, including lag-one Markov chain model, two-step model, and transition probability model to generate stochastic daily rainfall data of long-term duration, using data from a catchment in Australia. Three variations of lag-one Markov chain models were used: untransformed, logarithmic transformation, and square root transformation. Two-step model uses Markov chain to model rainfall occurrences and gamma distribution to model rainfall depths. Six variations of the Transition Probability Matrices were used, 3 using Shifted Exponential Distribution and 3 using Box-Cox Power Transformation was adopted to predict the high rainfall depths, and the parameters are determined using maximum-likelihood method on the available rainfall data. The models' results were tested by comparing the statistics of the generated data against those of the available data. Direct comparisons of the means, standard deviations, and skews show satisfactory results. Further comparisons of monthly means, standard deviations, skews, maxima and minima, as well as the lengths of wet and dry spells had also shown satisfactory results. In conclusion, all the models have produced synthetic rainfall data, which are statistically similar to those of the available data. In comparison, the TPM model gave the most accurate results. Therefore, this model may be utilised for synthetic rainfall data generations, which can then be used for forecasting.
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