Statistical Modeling of RPCA-FCM in Spatiotemporal Rainfall Patterns Recognition

This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis (PCA) coupled with the clustering approach, is often applied to reduce the dimension of the data while simultaneously performing cluster partitions. However, the classical PCA is highly insensitive to outliers, as it assigns equal weights to each set of observations. Hence, applying the classical PCA could affect the cluster partitions of the rainfall patterns. Furthermore, traditional clustering algorithms only allow each element to exclusively belong to one cluster, thus observations within overlapping clusters of the torrential rainfall datasets might not be captured effectively. In this study, a statistical model of torrential rainfall pattern recognition was proposed to alleviate these issues. Here, a Robust PCA (RPCA) based on Tukey’s biweight correlation was introduced and the optimum breakdown point to extract the number of components was identified. A breakdown point of 0.4 at 85% cumulative variance percentage efficiently extracted the number of components to avoid low-frequency variations or insignificant clusters on a spatial scale. Based on the extracted components, the rainfall patterns were further characterized based on cluster solutions attained using Fuzzy C-means clustering (FCM) to allow data elements to belong to more than one cluster, as the rainfall data structure permits this. Lastly, data generated using a Monte Carlo simulation were used to evaluate the performance of the proposed statistical modeling. It was found that the proposed RPCA-FCM performed better using RPCA-FCM compared to the classical PCA coupled with FCM in identifying the torrential rainfall patterns of Peninsular Malaysia’s East Coast.

Comparative Study of Abiotic Stress Factors on GC-MS-Detected Phytoconstituents of Aloe greatheadii var: davyana Using Heat Map and Hierarchical Clustering Dendrogram

Aloe greatheadii var. davyana or spotted aloe is indigenous to South Africa and widely distributed in the northern provinces. The plant has a vast ethnopharmacological application which is mostly attributed to its phytochemical content. The aim of the study was to examine the effect of abiotic stress factors on the plant’s phytochemical content. The phytochemical content of A. greatheadii hexane extracts from four different provinces (Limpopo, Mpumalanga, Gauteng, and North West), harvested from the wild at varied altitudes, rainfall patterns, and soil types, was examined using gas chromatography-mass spectra (GC-MS). The phytochemical content of hexane extracts from the four South African provinces was analysed using heat map analysis and hierarchical clustering dendrogram. The phytochemical content of A. greatheadii hexane extracts was composed of fatty acids, alkanes, benzene, carboxylic acids, ketones, phytosterols, and vitamins. Eicosane, henicosane, and [(2S)-2-[(2R)-4-hexadecanoyloxy-3-hydroxy-5-oxo-2H-furan-2-yl]-2-hydroxyethyl] hexadecanoate were the only compounds detected in all samples from the four provinces. The concentration levels of 2-(((2-ethylhexyl)oxy)carbonyl) benzoic acid, beta-sitosterol, tritetracontane, and ethyl 13-methyltetradecanoate were closely related and expressed a low clustering distance amongst the samples. Variations in soil pH, soil type, and rainfall patterns were detected and differed in the four provinces. The different abiotic stress factors affected the biochemical pathways for the different compounds, with conditions in Gauteng being less favourable for many of the compounds detected. Abiotic stress factors have shown to influence phytochemical biochemical pathways and quantity. Aloe greatheadii plants can be selected based on location seemingly due to the variations that persist in their phytochemical content.

Turbidity Informed Real-Time Control of a Dry Extended Detention Basin: A Case Study

Dry extended detention basins are static stormwater infrastructure, unable to adapt to shifts in water quality caused by urbanization in their source watersheds or long-term changes in rainfall patterns. As...

A study on the characterization of monsoon rainfall for sorghum (Sorghum Vulgare Pers) and its response to typical rainfall patterns

Twenty eight years (1962-89) rainfall of Nagpur was analysed and the rainfall suitability at various probability levels for sorghum crop was studied. The total rainfall during kharif season (25-39th MW) was 861.50 mm. Normal rainfall/week exceeded 50 mm during 12 weeks (25-36th MW) which declined successively for three week (37-39th MW). The coefficient of variation (CY) ranged between 74.3% (25th MW), to 144.7% (39th MW). The rainfall at 50% probability level was well distrturbed during 12 week (25-36th MW)-ranging between 44.5 to 36.3 mm being adequate and sufficient (>20 mm/week) for sorghum crop considering its weekly demand (21-35 mm/week). Typical rainfall patterns representing the situation were defined on the basis of their repetitiveness. Out of four typical rainfall patterns studied the one with lowest rainfall (458.4 mm /season and 30.56 mm/week) fetched the highest yield (865.0 kg/ha) which ensured adequate rains during the various growth stages except maturity. Excessive rainfall (>l00 mm/week) and deficient rainfall <20 mm/week) during every stage were inadequate. Rainfall atleast 30.56 mm/week was most adequate.

The Effects of Rainfall Patterns on Runoff, Sediment, and Nutrients Under Various Artificial Rainfall Experiments

Assessing the effects of rainfall patterns on runoff, sediment, nutrients under variation of rainfall pattern are significant in the quantification of sediment transported by overland flow. Previous experimental and field works studied that sediment transport is influenced by hydraulic properties of flow, physical properties of soil and surface characteristics. This study aims at determining the effect of rainfall patterns on surface runoff, sediment loss and nutrient loss. Experiments were carried out using four rainfall patterns, namely Pattern A (uniform-type: 8-8-8 l/min), Pattern B (increasing-type: 7-8-9 l/min), Pattern C (increasing-decreasing-type: 7-9-8 l/min) and Pattern D (decreasing-type: 9-8-7 l/min) with the changes of intensity every 30 minutes that gives total rainfall duration of 90 minutes for each pattern. The simulation was performed in three repetitions. The average total runoff produced was 668.65, 701.40, 699.10, and 722.63 liters, for rainfall patterns A, B, C, and D, respectively. The trend of runoff generated was influenced by the rainfall patterns, Pattern D generated the highest amount of runoff meanwhile Pattern A generated the lowest. For total suspended sediment concentrations, the mean value among every three repetitions of rainfall pattern resulted as 14,518.88, 13,732.73, 8,011.71 and 19,918.50 mg/l for patterns A, B, C, and D, respectively Pattern D contributed to the highest amount of sediment accumulated whereby Pattern C generated the lowest sediment despite the trend showed a different approach than the other 3 patterns. In nutrient concentrations, the determined total losses for ammonia nitrogen were 3.986, 2.891, 3.504, and 4.601g; nitrate nitrogen were 3.934, 2.665, 4.008, and 3.259g; phosphorus were 1.346, 0.222, 0.207, and 0.679g, for patterns A, B, C, and D, respectively. In general, rainfall pattern does have a significant impact on the trend of nutrient losses, where the trend shows that higher concentrations at the start and eventually lowered through the end, but Pattern D as compared to other patterns resulted in a more severe nutrient loss. For the affected area of the soil movement process, the calculated means of the affected area are 79.60, 68.70, 72.43, and 64.97% for patterns A, B, C, and D respectively. The lowest mean of the affected area is contributed by Pattern D and the highest by Pattern A.