scholarly journals Mapping the Spatiotemporal Diversity of Precipitation in Iran

2021 ◽  
Author(s):  
Zahra Jamshidi ◽  
Nozar Samani
Keyword(s):  
Hierarchical Clustering ◽  
Water Availability ◽  
Pearson Correlation ◽  
Principal Component ◽  
Rain Gauge ◽  
Classification Performance ◽  
Optimal Number ◽  
Annual Rainfall ◽  
Monthly Precipitation ◽  
Precipitation Water

Abstract Despite located in a semi-arid and arid part of the world, Iran enjoys a very diverse climate. As a result, water availability in different regions of the country is in a veil of ambiguity. To have a better insight, we investigate the spatiotemporal diversity of precipitation over the country by analyzing the 33-years long monthly precipitation time series (1983-2016) at 461 measuring rain-gauge stations. Cluster Analysis (CA) both hierarchical and non-hierarchical clustering approaches and Principal Component Analysis (PCA) was used to determine the homogeneous precipitation zones at three macro, meso, and micro-scales. First, the country is divided into six precipitation macro-regions using CA. Each region shows a unique mean annual hyetograph and is influenced by a particular air moisture mass entering the country. Then, the six regions were divided into 10 regions of meso-resolution through Hierarchical clustering (HC) and K-Means Clustering. Finally, an optimal number of 24 micro-zones is established that reflect a comprehensive precipitation map over the country, employing PCA, Hierarchical clustering, and K-Means Clustering. The annual hyetograph of each zone showed a unique pattern and distribution with a varying magnitude of monthly precipitation compared to others. The long-term (i.e., 33-years) mean annual rainfall in each region and zone is calculated, and the monthly and annual-precipitation water availability in the country is estimated. The result gives an accurate insight into the amount of precipitation that is expected to fall in each zone during each month of the year, that may be used as the reference for the prediction of the dry and wet seasons and years and also for the allocation of the harvested precipitation water to different consumptive sectors. The result shows that the Hierarchical clustering and PCA have significant classification performance in meso and micro- climatological zoning. Also, it was observed that there are significant similarities between the PCA and Hierarchical clustering (Ward’s method-Pearson correlation) results in micro-climatological zoning.

scholarly journals Geographic segmentation, spatial dependencies, and evaluation of the relative position of rain-gauges based on gridded data of mean monthly precipitation: application in Nigeria

2016 ◽  
Vol 49 (1) ◽  
pp. 107-122 ◽  
Author(s):  
V. G. Aschonitis ◽  
G. O. Awe ◽  
T. P. Abegunrin ◽  
K. A. Demertzi ◽  
D. M. Papamichail ◽  
...  
Keyword(s):  
Relative Position ◽  
Principal Component ◽  
Spatiotemporal Analysis ◽  
Rain Gauge ◽  
Spatiotemporal Variability ◽  
Monthly Precipitation ◽  
Rain Gauges ◽  
Spatial Dependencies ◽  
The Mean ◽  
Rain Gauge Network

Abstract The aim of the study is to present a combination of techniques for (a) the spatiotemporal analysis of mean monthly gridded precipitation datasets and (b) the evaluation of the relative position of the existing rain-gauge network. The mean monthly precipitation (P) patterns of Nigeria using ∼1 km2 grids for the period 1950–2000 were analyzed and the position of existing rain-gauges was evaluated. The analysis was performed through: (a) correlations of P versus elevation (H), latitude (Lat) and longitude (Lon); (b) principal component analysis (PCA); (c) Iso-Cluster and maximum likelihood classification (MLC) analysis for terrain segmentation to regions with similar temporal variability of mean monthly P; (d) use of MLC to create reliability classes of grid locations based on the mean clusters’ characteristics; and (e) analysis to evaluate the relative position of 33 rain-gauges based on the clusters and their reliability classes. The correlations of mean monthly P versus H, Lat, Lon, and PCA highlighted the spatiotemporal effects of the Inter Tropical Discontinuity phenomenon. The cluster analysis revealed 47 clusters, of which 22 do not have a rain-gauge while eight clusters have more than one rain-gauge. Thus, more rain-gauges and a better distribution are required to describe the spatiotemporal variability of P in Nigeria.

scholarly journals ANALYSIS OF TREND OF THE PRECIPITATION DATA: A CASE STUDY OF KANGRA DISTRICT, HIMACHAL PRADESH

2015 ◽  
Vol 3 (9) ◽  
pp. 87-95
Author(s):  
Arijit Ganguly ◽  
Ranjana Ray Chaudhuri ◽  
Prateek Sharma
Keyword(s):  
Flood Control ◽  
Rain Gauge ◽  
Himachal Pradesh ◽  
Flood Frequency ◽  
Negative Trend ◽  
Annual Rainfall ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Level Of Significance

The current study is carried out to determine the potential trend of rainfall and assess its significance in Kangra district of Himachal Pradesh. Rainfall is a key characteristic of any watershed which plays a significant role in flood frequency, flood control studies and water planning and management. In this case study,mean monthly rainfall has been analysed to determine the variability in magnitude over the period 1950-2005.  Trend in mean monthly precipitation data and mean seasonal trends are analysed using Mann-Kendall test and Sen’s slope estimation for the data period 1950-2005. Analysis of monthly trend in precipitation shows negative trend for the months of July, August, September and October in all the rain gauge stations. However, the falling trend is significant for the month of August for Dharamshala(0.05 level of significance). Interestingly the month of June shows rising trend of rainfall in all the stations, however, at Dharamshala the trend is significant (0.01 level of significance). The winter rainfall in the month of January and February record decreasing trend, with DeraGobipur and Kangra recording significant decreasing trend for the month of January at 0.01 level of significance and 0.05 level of significance respectively. Trend analysis for annual rainfall data shows significant negative trend for Dharamshala.

scholarly journals Hybrid Hierarchical Clustering — Piecewise Aggregate Approximation, with Applications

2016 ◽  
Vol 15 (04) ◽  
pp. 1650019 ◽  
Author(s):  
Yu Zhang ◽  
Michael Gallimore ◽  
Chris Bingham ◽  
Jun Chen ◽  
Yong Xu
Keyword(s):  
Hierarchical Clustering ◽  
Gas Turbines ◽  
Principal Component ◽  
Classification Performance ◽  
Computationally Efficient ◽  
Efficient Tool ◽  
Piecewise Constant ◽  
Industrial Gas Turbines ◽  
Rich Data ◽  
Piecewise Constant Approximation

Piecewise Aggregate Approximation (PAA) provides a powerful yet computationally efficient tool for dimensionality reduction and Feature Extraction (FE) on large datasets compared to previously reported and well-used FE techniques, such as Principal Component Analysis (PCA). Nevertheless, performance can degrade as a result of either regional information insufficiency or over-segmentation, and because of this, additional relatively complex modifications have subsequently been reported, for instance, Adaptive Piecewise Constant Approximation (APCA). To recover some of the simplicity of the original PAA, whilst addressing the known problems, a distance-based Hierarchical Clustering (HC) technique is now proposed to adjust PAA segment frame sizes to focus segment density on information rich data regions. The efficacy of the resulting hybrid HC-PAA methodology is demonstrated using two application case studies viz. fault detection on industrial gas turbines and ultrasonic biometric face identification. Pattern recognition results show that the extracted features from the hybrid HC-PAA provide additional benefits with regard to both cluster separation and classification performance, compared to traditional PAA and APCA alternatives. The method is therefore demonstrated to provide a robust and readily implemented algorithm for rapid FE and identification for datasets.

scholarly journals On the Epochal Strengthening in the Relationship between Rainfall of East Africa and IOD

2013 ◽  
Vol 26 (15) ◽  
pp. 5655-5673 ◽  
Author(s):  
Desmond Manatsa ◽  
Swadhin K. Behera
Keyword(s):  
East Africa ◽  
Rainfall Variability ◽  
Spatial Coherence ◽  
Principal Component ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Global Precipitation Climatology Centre

Abstract Variability of the equatorial East Africa “short rains” (EASR) has intensified significantly since the turn of the twentieth century. This increase toward more extreme rainfall events has not been gradual but is strongly characterized by epochs. The rain gauge–based Global Precipitation Climatology Centre (GPCC) monthly precipitation dataset for the period 1901–2009 is used to demonstrate that the epochal changes were dictated by shifts in the Indian Ocean dipole (IOD) mode. These shifts occurred during 1961 and 1997. In the pre-1961 period, there was virtually no significant linear link between the IOD and the EASR. But a relatively strong coupling between the two occurred abruptly in 1961 and was generally maintained at that level until 1997, when another sudden shift to even a greater level occurred. The first principal component (PC1) extracted from the EASR spatial domain initially merely explained about 50% of the rainfall variability before 1961, and then catapulted to about 73% for the period from 1961 to 1997, before eventually shifting to exceed 82% after 1997. The PC1 for each successive epoch also displayed loadings with notably improved spatial coherence. This systematic pattern of increase was accompanied by both a sharp increase in the frequency of rainfall extremes and spatial coherence of the rainfall events over the region. Therefore, it is most likely that the 1961 and 1997 IOD shifts are responsible for the epochal modulation of the EASR in both the spatial and temporal domain.

scholarly journals Spatial and temporal patterns of precipitation in Montenegro

Időjárás ◽  
2020 ◽  
Vol 124 (4) ◽  
pp. 499-519
Author(s):  
Golub Ćulafić ◽  
Tatjana Popov ◽  
Slobodan Gnjato ◽  
Davorin Bajić ◽  
Goran Trbić ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Hierarchical Clustering ◽  
Principal Component ◽  
Temporal Patterns ◽  
Summer Season ◽  
Monthly Precipitation ◽  
Agglomerative Hierarchical Clustering ◽  
Clustering Method ◽  
Spatial And Temporal Patterns ◽  
Precipitation Regimes

The paper analyses, spatial and temporal patterns of precipitation over Montenegro. Data on mean monthly precipitation during the period 1961–2015 from 17 meteorological stations were used for the analysis. Four regions with different spatial precipitation regimes were identified by using the principal component analysis and the agglomerative hierarchical clustering method. A downward tendency in annual precipitation prevails over Montenegro. The most prominent reduction was present in the summer season. In contrast, precipitation increased during autumn. However, the majority of estimated trend values was low and statistically insignificant.

scholarly journals Estimating the rainfall erosivity factor from monthly precipitation data in the Madrid Region (Spain)

2015 ◽  
Vol 63 (1) ◽  
pp. 55-62 ◽  
Author(s):  
David Hernando ◽  
Manuel G. Romana
Keyword(s):  
Mean Squared Error ◽  
Rain Gauge ◽  
Accurate Estimate ◽  
Coefficient Of Determination ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
Precipitation Concentration Index ◽  
R Factor ◽  
Record Length

Abstract The need for continuous recording rain gauges makes it difficult to determine the rainfall erosivity factor (Rfactor) of the Universal Soil Loss Equation in regions without good spatial and temporal data coverage. In particular, the R-factor is only known at 16 rain gauge stations in the Madrid Region (Spain). The objectives of this study were to identify a readily available estimate of the R-factor for the Madrid Region and to evaluate the effect of rainfall record length on estimate precision and accuracy. Five estimators based on monthly precipitation were considered: total annual rainfall (P), Fournier index (F), modified Fournier index (MFI), precipitation concentration index (PCI) and a regression equation provided by the Spanish Nature Conservation Institute (RICONA). Regression results from 8 calibration stations showed that MFI was the best estimator in terms of coefficient of determination and root mean squared error, closely followed by P. Analysis of the effect of record length indicated that little improvement was obtained for MFI and P over 5- year intervals. Finally, validation in 8 additional stations supported that the equation R = 1.05·MFI computed for a record length of 5 years provided a simple, precise and accurate estimate of the R-factor in the Madrid Region.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

scholarly journals Bioprospecting of Sechium spp. varieties for the selection of characters with pharmacological activity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
María Isabel Iñiguez-Luna ◽  
Jorge Cadena-Iñiguez ◽  
Ramón Marcos Soto-Hernández ◽  
Francisco Javier Morales-Flores ◽  
Moisés Cortes-Cruz ◽  
...  
Keyword(s):  
Cell Lines ◽  
Proliferative Activity ◽  
Meta Analysis ◽  
Pearson Correlation ◽  
Statistical Significance ◽  
Economic Value ◽  
Principal Component ◽  
Malignant Cell ◽  
Length Polymorphisms ◽  
Relevant Variables

AbstractBioprospecting identifies new sources of compounds with actual or potential economic value that come from biodiversity. An analysis was performed regarding bioprospecting purposes in ten genotypes of Sechium spp., through a meta-analysis of 20 information sources considering different variables: five morphological, 19 biochemical, anti-proliferative activity of extracts on five malignant cell lines, and 188 polymorphic bands of amplified fragment length polymorphisms, were used in order to identify the most relevant variables for the design of genetic interbreeding. Significant relationships between morphological and biochemical characters and anti-proliferative activity in cell lines were obtained, with five principal components for principal component analysis (SAS/ETS); variables were identified with a statistical significance (< 0.7 and Pearson values ≥ 0.7), with 80.81% of the accumulation of genetic variation and 110 genetic bands. Thirty-nine (39) variables were recovered using NTSYSpc software where 30 showed a Pearson correlation (> 0.5) and nine variables (< 0.05), Finally, using a cladistics analysis approach highlighted 65 genetic bands, in addition to color of the fruit, presence of thorns, bitter flavor, piriform and oblong shape, and also content of chlorophylls a and b, presence of cucurbitacins, and the IC50 effect of chayote extracts on the four cell lines.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

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