Analysis and presentation of rainfall data

2020 ◽  
pp. 539-546
Author(s):  
Amithirigala Widhanelage Jayawardena
Keyword(s):  
Rainfall Data

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

CORRELATION BETWEEN RAINFALL PATTERN AND OIL PALM PERFORMANCE IN SUMATRA AND BORNEO ISLAND DURING EL NIÑO 2015

2018 ◽  
Vol 24 (2) ◽  
pp. 87-96
Author(s):  
Iput Pradiko ◽  
Eko Novandy Ginting ◽  
Nuzul Hijri Darlan ◽  
Winarna Winarna ◽  
Hasril Hasan Siregar
Keyword(s):  
Oil Palm ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Pearson Correlation ◽  
Rainfall Data ◽  
Rainfall Pattern ◽  
Dry Spell ◽  
Male Flowers ◽  
Central South

El Niño 2015 is one of the strongest El Niño. Drought stress due to El Niño could affect oil palm performances. This study was conducted to determine rainfall pattern and oil palm performance in Sumatra and Borneo Island during El Niño 2015. Data employed in this study is monthly rainfall data, Southern Oscillation Index (SOI) January-December 2015, andoil palm performances. Pearson correlation between SOI and rainfall data was used to analyze rainfall pattern, while oil palm performances were observed based on morphological conditions. Result shows that southern part of Sumatra and mostly part of Borneo suffer from more dry spell, dry month, and water deficit such as 37-133 days, 3-5 months, and 349-524 mm respectively. Analysis of rainfall pattern shows that Jambi, South Sumatra, Lampung, Central, South, and East Borneo are significantly (r ≥ +0,60) affected by El Niño 2015. Oil palms in southern part of Sumatra and mostly part of Borneo are suffer from drought stressmarked by the emergence of more than two spear fronds, appearing of many male flowers, malformations on bunches, fronds tend to hanging down, and lower fronds tend to dry.

scholarly journals Intra-Storm Pattern Recognition through Fuzzy Clustering

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 57
Author(s):  
Konstantinos Vantas ◽  
Epaminondas Sidiropoulos
Keyword(s):  
Fuzzy Clustering ◽  
A Priori ◽  
Temporal Distribution ◽  
Distribution Patterns ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Domain Specific ◽  
Runoff Modeling ◽  
National Bank ◽  
Rainfall Patterns

The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former.

scholarly journals Comparison of Missing Rainfall Data Treatment Analysis at Kenyir Lake

2021 ◽  
Vol 1144 (1) ◽  
pp. 012046
Author(s):  
Azreen Harina Azman ◽  
Nurul Nadrah Aqilah Tukimat ◽  
M A Malek
Keyword(s):  
Rainfall Data ◽  
Data Treatment ◽  
Treatment Analysis
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