scholarly journals Weather Types and Rainfall over Senegal. Part II: Downscaling of GCM Simulations

2008 ◽  
Vol 21 (2) ◽  
pp. 288-307 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
M. Neil Ward ◽  
Ousmane Ndiaye
Keyword(s):  
Interannual Variability ◽  
Nearest Neighbor ◽  
Daily Rainfall ◽  
K Nearest Neighbor ◽  
Wind Fields ◽  
Weather Types ◽  
Local Scaling ◽  
Rainfall Frequency ◽  
Dry Spells ◽  
The Mean

Abstract Four methods of downscaling daily rainfall sequences from general circulation model (GCM) simulations are intercompared over Senegal, using a 13-station network of daily observations during July–September 1961–98. The local scaling method calibrates raw GCM daily rainfall at the closest grid point to a given station so that the climatological distribution of rainfall matches the observed one. The k-nearest neighbor and weather classification schemes resample historical station rainfall observations according to the similarity between the daily wind fields from an ensemble of GCM simulations and a historical library of reanalysis [from 40-yr ECMWF Re-Analysis (ERA-40)] daily wind fields. The nonhomogenous hidden Markov model uses a small set of hidden states to describe the relationship between daily station rainfall observations and low-pass-filtered simulated winds to simulate stochastic sequences of daily rainfall. The four methods are assessed in terms of seasonal statistics of daily rainfall, including seasonal amount, rainfall frequency, and the mean length of wet and dry spells. Verification measures used are mean bias error, interannual anomaly correlation, root-mean-square error, and ranked probability skill score. The k-nearest neighbor and weather-type classification are shown to perform similarly well in reproducing the mean seasonal cycle, interannual variability of seasonal amount, daily rainfall frequency, as well as the mean length of dry and wet spells, and generally slightly better than the nonhomogeneous hidden Markov model. All three methods are shown to outperform the simple local scaling method. This is due to (i) the ability of the GCM to reproduce remarkably well the mean seasonal cycle and the transitions between weather types defined from reanalysis and (ii) the GCM’s moderate-to-strong skill in reproducing the interannual variability of the frequency occurrence of the weather types that strongly influence the interannual variability of rainfall in Senegal. In contrast, the local scaling exaggerates the length of wet and dry spells and reproduces less accurately the interannual variability of the seasonal-averaged amounts, occurrences, and dry/wet spells. This failure is attributed primarily to systematic errors in the GCM’s precipitation simulation.

scholarly journals Variability and the Severity of the “Little Dry Season” in Southwestern Nigeria

2006 ◽  
Vol 19 (3) ◽  
pp. 483-493 ◽  
Author(s):  
James O. Adejuwon ◽  
Theophilus O. Odekunle
Keyword(s):  
Interannual Variability ◽  
Agricultural Practices ◽  
Daily Rainfall ◽  
Walker Circulation ◽  
Dry Season ◽  
Total Rainfall ◽  
Southwestern Nigeria ◽  
The Mean ◽  
Period Of Occurrence ◽  
River Niger

Abstract The Little Dry Season (LDS) of West Africa is manifested as a decline in both the frequency and amount of daily rainfall for a number of weeks halfway through the rainy season. The mean or climatological LDS is derived from the slope of the cumulative percentage graph of 5-day mean rainfall (daily rainfall data between 1961 and 2000). LDS variability analysis was carried out using the concept of relative variability. The results obtained showed that LDS is observed from mid-July to mid-September along the coast. Northward and eastward the period of occurrence decreases. In general, the phenomenon is not observed north of the eastward-flowing or east of the southward-flowing River Niger. The results also show considerable interannual variability. Variability was highest along the southwestern coast and declined inland northward and eastward. Variability was highest with respect to total rainfall, followed by length and number of rain days. There are indications that for most years the LDS was only relatively dry while in certain years it represented a period of drought. The occurrence of the LDS in space and time is explained by the movements of the intertropical discontinuity and its associated zone of rainfall. Interannual variability in occurrence and severity are determined by the Walker Circulation phenomenon. Variability in the severity of the LDS has mixed implications for agricultural practices.

scholarly journals PERBANDINGAN METODE K-NN DAN BAYES PADA MISSING IMPUTATION

2019 ◽  
Vol 5 (2) ◽  
pp. 85-90
Author(s):  
Taufiq Rizaldi ◽  
Fendik Eko Purnomo ◽  
Aji Seto Arifianto
Keyword(s):  
Data Collection ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Loss ◽  
Optimal Outcome ◽  
K Nearest Neighbor ◽  
Bayes Methods ◽  
Data Collection Process ◽  
The Mean

The problem of data loss in a dataset is experienced in surveys for data collection which are usually caused by no response from units or items during the survey data collection process. The loss of a data can significantly influence the results of a study. The inaccuracy in choosing a solution to overcome these problems can result in a less than optimal outcome that tends to be biased. Some methods that are widely used to solve these problems are using the K Nearest Neighbor (K-NN) and Naïve Bayes methods, the main purpose of this study is to compare the performance of the two methods. From the results of the K-NN, the results were better, where the Mean Square Error (MSE) is bigger than 1 and MAPE around 10-16%, while Naïve Bayes got MSE values bigger than 1 and MAPE ​​around 26%.

scholarly journals Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1523
Author(s):  
Hana Charvátová ◽  
Aleš Procházka ◽  
Oldřich Vyšata
Keyword(s):  
Heart Rate ◽  
Nearest Neighbor ◽  
Digital Processing ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Mean Power ◽  
Frequency Bands ◽  
The Mean ◽  
Motion Assessment

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands ⟨ 3 , 8 ⟩ and ⟨ 8 , 15 ⟩ Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.

scholarly journals A study on effective monsoon and dry spells of short return periods The Indian mountain ranges during monsoon months in two north Bengal districts

MAUSAM ◽  
2021 ◽  
Vol 47 (2) ◽  
pp. 145-148
Author(s):  
A. D. DAS ◽  
S. K. MUKHOPADHYAY
Keyword(s):  
Monsoon Season ◽  
Daily Rainfall ◽  
Return Periods ◽  
Mountain Ranges ◽  
Dry Spells ◽  
Rainfed Farming ◽  
Dry Spell ◽  
Expected Length ◽  
The Mean ◽  
North Bengal

This article uses daily rainfall data (April-October) of Cooch Behar (1971-90) and Jalpaiguri (1972-90), the two predominantly rainfed farming districts of Terai zone of West Bengal, to study the, nature of different rainfall parameters of this area. It was observed that the mean date of Onset of Effective Monsoon (OEM) of this region is about one month in advance from the normal occurrence of monsoon over Kerala. However, the monsoon rains, here, retreat at about the same time with those of  Kerala. Distribution of the duration of dry spell has been studied to have some idea of the nature of critical dry spells during the monsoon season. The article also examines how prolonged, on the average, are the monsoon breaks for different return periods. Expected length of dry spell (in days) for 2, 5, 10 and 20 years return periods have been estimated with the help of suitably fitted curves for each location.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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