scholarly journals Clustering technique for interpretation of cloudburst over Uttarakhand

MAUSAM ◽  
2021 ◽  
Vol 67 (3) ◽  
pp. 669-676
Author(s):  
KAVITA PABREJA ◽  
RATTAN K. DATTA
Keyword(s):  
Data Mining ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Large Data ◽  
Database Systems ◽  
Numerical Weather Prediction Model ◽  
Primary Data ◽  
Small Scale ◽  
Data Mining Technique ◽  
Clustering Technique

Data Mining has been used extensively in various business and scientific applications for last few years. Data mining has been found to be providing a deep insight into understanding the hidden facts in huge databases. Data mining is an interdisciplinary subfield of computer science that discovers patterns in large data sets by using methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. In this paper, data mining technique for Interpretation of Weather Forecasts for one of the most disastrous weather phenomenon viz. cloudburst has been applied. Every year, cloudburst over hilly areas and coastal regions causes loss of lives and property. The forecasting and warning of these events is very difficult. There is no satisfactory technique for anticipating the occurrence of cloudbursts because of their small scale. A very fine network of radars is required to be able to detect the likelihood of a cloudburst and this would be prohibitively expensive. The warning of cloudburst could only be provided at a small lead time say a few hours in advance based on the interpretation of latest satellite imagery data, powerful radar (Doppler category), if available, or by using Model Output Statistics (MOS) models. Another dimension to forecasting this weather event has been identified by applying clustering technique on primary data forecasted by global and regional models of weather forecasting. A recent case of Cloudburst over Uttarakhand that caused a huge loss has been analyzed using k-means clustering technique of data mining. It has been observed that with the mining of Numerical Weather Prediction model forecast data, the signals of formation of cloudburst can be found3-4 days in advance.

scholarly journals A Data Mining Approach for Parameter Optimization in Weather Prediction

2020 ◽  
Vol 1 (1) ◽  
pp. 1-13
Author(s):  
Thushika N ◽  
Premaratne S
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Weather Data ◽  
Quality Model ◽  
Data Mining Technique ◽  
Related Data ◽  
Time To Build ◽  
Effective Manner

More than two decades, there is a number of weather-related websites are available which approximately predict the weather and climate. By extracting essential data from the websites, a predictive data pattern can be produced to show the next day’s weather is with rain or not.  By applying different types of web mining and analyzing techniques those extracted weather-related data can be visualized to a typical pattern for weather forecasting with the main deciding factors of weather. With the use of these approaches, reasonably precise forecasts can be made up to about four to five days in advance. For the weather prediction analysis, we need to discover deciding factors of the next day’s weather. Particularly, common weather dependent factors and the relationship of the prediction to the particular phenomenon. The solution proposed by this research can be used to analyze a large amount of weather data which are in different forms in each source. By using predictive mining task our solution allows us to make predictions for future instances according to the model what we have created. Evaluation measurements for the selected data mining technique such as accuracy percentage, TP & FP Rate, Precision, F-Measure, ROC area, SSE, and loglikelihood for classification and clustering leads to create a high-quality model of prediction. Knowledge flow interface provides the data flow to show the processing and analyzing data with precise association rules. In order to evaluate the model, SSE values and time to build the model, are considered in an effective manner.

scholarly journals Probabilistic Forecasts of Mesoscale Convective System Initiation Using the Random Forest Data Mining Technique

2016 ◽  
Vol 31 (2) ◽  
pp. 581-599 ◽  
Author(s):  
David Ahijevych ◽  
James O. Pinto ◽  
John K. Williams ◽  
Matthias Steiner
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Weather Prediction ◽  
False Alarms ◽  
Convective System ◽  
Training Set ◽  
Data Mining Technique ◽  
High Detection Rate ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective

Abstract A data mining and statistical learning method known as a random forest (RF) is employed to generate 2-h forecasts of the likelihood for initiation of mesoscale convective systems (MCS-I). The RF technique uses an ensemble of decision trees to relate a set of predictors [in this case radar reflectivity, satellite imagery, and numerical weather prediction (NWP) model diagnostics] to a predictand (in this case MCS-I). The RF showed a remarkable ability to detect MCS-I events. Over 99% of the 550 observed MCS-I events were detected to within 50 km. However, this high detection rate came with a tendency to issue false alarms either because of premature warning of an MCS-I event or in the continued elevation of RF forecast likelihoods well after an MCS-I event occurred. The skill of the RF forecasts was found to increase with the number of trees and the fraction of positive events used in the training set. The skill of the RF was also highly dependent on the types of predictor fields included in the training set and was notably better when a more recent training period was used. The RF offers advantages over high-resolution NWP because it can be run in a fraction of the time and can account for nonlinearly varying biases in the model data. In addition, as part of the training process, the RF ranks the importance of each predictor, which can be used to assess the utility of new datasets in the prediction of MCS-I.

scholarly journals Personalized web search on e-commerce using ontology based association mining

2017 ◽  
Vol 7 (1.1) ◽  
pp. 286
Author(s):  
B. Sekhar Babu ◽  
P. Lakshmi Prasanna ◽  
P. Vidyullatha
Keyword(s):  
Data Mining ◽  
Web Search ◽  
Large Data ◽  
Association Mining ◽  
Data Sets ◽  
Data Mining Algorithm ◽  
Web Data ◽  
Data Mining Technique ◽  
Web Data Mining ◽  
The Web

 In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.

scholarly journals Does the Representation of Flow Structure and Turbulence at a Cold Front Converge on Multiscale Observations with Model Resolution?

2017 ◽  
Vol 145 (11) ◽  
pp. 4345-4363 ◽  
Author(s):  
Ben Harvey ◽  
John Methven ◽  
Chloe Eagle ◽  
Humphrey Lean
Keyword(s):  
Flow Structure ◽  
Cold Front ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Turbulent Fluxes ◽  
Numerical Weather Prediction Model ◽  
Shear Instability ◽  
Small Scale ◽  
Convective Boundary ◽  
Direct Evaluation

In situ aircraft observations are used to interrogate the ability of a numerical weather prediction model to represent flow structure and turbulence at a narrow cold front. Simulations are performed at a range of nested resolutions with grid spacings of 12 km down to 100 m, and the convergence with resolution is investigated. The observations include the novel feature of a low-altitude circuit around the front that is closed in the frame of reference of the front, thus allowing the direct evaluation of area-average vorticity and divergence values from circuit integrals. As such, the observational strategy enables a comparison of flow structures over a broad range of spatial scales, from the size of the circuit itself ([Formula: see text]100 km) to small-scale turbulent fluctuations ([Formula: see text]10 m). It is found that many aspects of the resolved flow converge successfully toward the observations with resolution if sampling uncertainty is accounted for, including the area-average vorticity and divergence measures and the narrowest observed cross-frontal width. In addition, there is a gradual handover from parameterized to resolved turbulent fluxes of moisture and momentum as motions in the convective boundary layer behind the front become partially resolved in the highest-resolution simulations. In contrast, the parameterized turbulent fluxes associated with subgrid-scale shear-driven turbulence ahead of the front do not converge on the observations. The structure of frontal rainbands associated with a shear instability along the front also does not converge with resolution, indicating that the mechanism of the frontal instability may not be well represented in the simulations.

Gridded Operational Consensus Forecasts of 2-m Temperature over Australia

2012 ◽  
Vol 27 (2) ◽  
pp. 301-322 ◽  
Author(s):  
Chermelle Engel ◽  
Elizabeth E. Ebert
Keyword(s):  
Bias Correction ◽  
Statistical Approach ◽  
Weather Prediction ◽  
Weighted Average ◽  
Numerical Weather Prediction Model ◽  
Small Scale ◽  
Coarse Scale ◽  
Consensus Forecasts ◽  
Average Consensus ◽  
Almost All

Abstract This paper describes an extension of an operational consensus forecasting (OCF) scheme from site forecasts to gridded forecasts. OCF is a multimodel consensus scheme including bias correction and weighting. Bias correction and weighting are done on a scale common to almost all multimodel inputs (1.25°), which are then downscaled using a statistical approach to an approximately 5-km-resolution grid. Local and international numerical weather prediction model inputs are found to have coarse scale biases that respond to simple bias correction, with the weighted average consensus at 1.25° outperforming all models at that scale. Statistical downscaling is found to remove the systematic representativeness error when downscaling from 1.25° to 5 km, though it cannot resolve scale differences associated with transient small-scale weather.

scholarly journals Precipitation Prediction for South West Monsoon Over Karnataka using Supervised Learning Technique

2019 ◽  
Vol 8 (3) ◽  
pp. 4450-4454
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Weather Forecasting ◽  
Grid Point ◽  
Time Series Prediction ◽  
Data Mining Technique ◽  
Data Framework ◽  
South West ◽  
South West Monsoon ◽  
West Monsoon

Weather forecasting is a major field of study in the area of Meteorology. Data Scientists, meteorologists and weather forecasters are implementing the experimentation of weather forecasting base on numerical and statistical methods. Traditional models used the fluid and thermal dynamic strategies for grid-point time series prediction based on few inherited constraints, such as the adoption of incomplete boundary rules, model assumptions and numerical instabilities. The nominated work is focused on finding the south west monsoon months’ precipitation patterns over the specific stations of Karnataka State. A multi-dimensional data framework for climate database with implementation online based data analysis has been developed. This works is carried out on the basis of monsoons that have prevailed during a year for the past 10 years. The proposed model emphasis the implementation of the association rules which has been extracted by the supervised classifier approach of data mining algorithms. The data mining technique of association rules emphasis the occurrence of the precipitation and will be helpful to take decisions in advance to the day to day operations in business, agriculture, water management and etc.

scholarly journals Sensitivity experiments of a severe rainfall event in north-western Italy: 17 August 2006

2008 ◽  
Vol 2 (1) ◽  
pp. 133-138 ◽  
Author(s):  
M. Milelli ◽  
E. Oberto ◽  
A. Parodi
Keyword(s):  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Precipitation Forecast ◽  
Small Scale ◽  
Moist Convection ◽  
Cosmo Model ◽  
Quantitative Precipitation Forecast ◽  
Scale Modelling ◽  
North Western ◽  
Priority Project

Abstract. This study is embedded into a wider project named "Tackle deficiencies in Quantitative Precipitation Forecast (QPF)'' in the framework of the COSMO (COnsortium for Small-scale MOdelling) community. In fact QPF is an important purpose of a numerical weather prediction model, for forecasters and customers. Unfortunately, precipitation is also a very difficult parameter to forecast quantitatively. This priority project aims at looking into the COSMO Model deficiencies concerning QPF by running different numerical simulations of various events not correctly predicted by the model. In particular, this work refers to a severe event (moist convection) happened in Piemonte region during summer 2006. On one side the results suggest that details in orography representation have a strong influence on accuracy of QPF. On the other side COSMO Model exhibits a poor sensitivity on changes in numerical and physical settings when measured in terms of QPF improvements. The conclusions, although not too general, give some hint towards the behaviour of the COSMO Model in a typical convective situation.

From data to knowledge mining

2009 ◽  
Vol 23 (4) ◽  
pp. 427-441 ◽  
Author(s):  
Ana Cristina Bicharra Garcia ◽  
Inhauma Ferraz ◽  
Adriana S. Vivacqua
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Evaluation Criteria ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Points

AbstractMost past approaches to data mining have been based on association rules. However, the simple application of association rules usually only changes the user's problem from dealing with millions of data points to dealing with thousands of rules. Although this may somewhat reduce the scale of the problem, it is not a completely satisfactory solution. This paper presents a new data mining technique, called knowledge cohesion (KC), which takes into account a domain ontology and the user's interest in exploring certain data sets to extract knowledge, in the form of semantic nets, from large data sets. The KC method has been successfully applied to mine causal relations from oil platform accident reports. In a comparison with association rule techniques for the same domain, KC has shown a significant improvement in the extraction of relevant knowledge, using processing complexity and knowledge manageability as the evaluation criteria.

scholarly journals Pre-Operational Application of a WRF-Hydro-Based Fluvial Flood Forecasting System in the Southeast Mediterranean

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 437-446
Author(s):  
Christos Giannaros ◽  
Elissavet Galanaki ◽  
Vassiliki Kotroni ◽  
Konstantinos Lagouvardos ◽  
Christina Oikonomou ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Weather Prediction ◽  
Warning System ◽  
Flood Forecasting ◽  
Numerical Weather Prediction Model ◽  
Operational Mode ◽  
Related Information ◽  
Modeling Methodology ◽  
Forecasting System ◽  
Warning Services

The Southeast Mediterranean (SEM) is characterized by increased vulnerability to river/stream flooding. However, impact-oriented, operational fluvial flood forecasting is far away from maturity in the region. The current paper presents the first attempt at introducing an operational impact-based warning system in the area, which is founded on the coupling of a state-of-the-art numerical weather prediction model with an advanced spatially-explicit hydrological model. The system’s modeling methodology and forecasting scheme are presented, as well as prototype results, which were derived under a pre-operational mode. Future developments and challenges needed to be addressed in terms of validating the system and increasing its efficiency are also discussed. This communication highlights that standard approaches used in operational weather forecasting in the SEM for providing flood-related information and alerts can, and should, be replaced by advanced coupled hydrometeorological systems, which can be implemented without a significant cost on the operational character of the provided services. This is of great importance in establishing effective early warning services for fluvial flooding in the region.

scholarly journals An Ingenious Methodology for the Collation of Existing Algorithms for the Prognosis of Student Performance

2020 ◽  
Vol 9 (5) ◽  
pp. 1749-1752
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Random Forest ◽  
Student Performance ◽  
Naive Bayes ◽  
Research Work ◽  
Large Data ◽  
Naïve Bayes ◽  
Impact Factors ◽  
Data Mining Technique

In this proposed research work we use a profound Data mining technique which is an automated procedure of discovering interesting patterns by means of comprehensible predictive models from large data sets by grouping them. Predicting a student's academic performance is very crucial especially for universities. Educational Data Mining (EDM) is an approach for extricating useful data that could possibly affect a firm. Nowadays student’s performance is swayed by a lot of aspects. These aspects might involve the academic performance of a student. This subject evaluates numerous factors probably suspected to alter a student’s empirical performance in scholastic, and discover a subjective design which classifies and forecast the student’s learning outcomes. The intention of this research is to conduct a case study on factors swayed by the student’s academic achievements and to dictate greater impact factors. In this paper we focus on the academic achievement evaluation on the basis of correct instances and incorrect instances by means of Naive Bayes and Random Forest algorithms. This paper intends to make a metaphorical assessment of Naive Bayes and random Forest classifier on student data and dictate the best algorithm.

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