№ files_lp_3_process_9_45787
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An academic review article examining data mining methods applied to precipitation forecasting and their relevance to agricultural planning and crop yield prediction.
Authors:
Vineeta Prakaulya; Prof. Roopesh Sharma; Upendra Singh; Ravikant Itare
Affiliation:
Patel College of Science and Technology, Indore
Department:
Computer Science and Engineering (CSE)
Document Type:
Review Article
Subject Area:
Data Mining and Climate Forecasting
Keywords:
Data mining, agriculture, soil fertility, crop yield, ANNs, FIS, LVQ, bi clustering
Geographical Focus:
India
Thematic Focus:
Precipitation prediction and agricultural productivity
Methods Discussed:
Artificial Neural Networks (ANNs), Fuzzy Inference System (FIS), Decision Tree (SLIQ), Time Series Analysis, Learning Vector Quantization (LVQ), Support Vector Machine (SVM), Bi-clustering
Data Scope:
Historical precipitation data including 45-year monsoon records and monthly precipitation series (1956–2008)
Intended Audience:
Researchers and practitioners in data mining, meteorology, and agricultural management
Price: 8 / 10 USD
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The product description is provided for reference. Actual content and formatting may differ slightly.
Year:
2022
Region / city:
Global
Subject:
Meteorology, Precipitation
Document Type:
Technical Report
Organization:
ITU-R Study Group 3
Author:
Working Party 3J
Target Audience:
Meteorologists, Atmospheric Scientists, Engineers
Period of validity:
Not specified
Approval Date:
14 June 2022
Date of Changes:
Not specified
Year:
Not specified
Program:
ITWCVPGIS Training
Day:
4
Objective:
Make production level maps with real-time forecasts
Task:
Replicate seasonal precipitation forecast maps over selected region
Data Sources:
NOAA CPC International Desk NMME probabilistic seasonal forecast; ne_10m_admin0_countries; ne_10m_ocean
Geographic Scope:
User-defined region of interest
Software:
GIS application supporting shapefiles and geoprocessing tools
File Created:
forecasts.shp
Map Elements:
Boundary map, ocean layer, forecast polygons, intersection layer
Output Format:
PDF and PNG
Forecast Categories:
Above-normal, Near-normal, Below-normal precipitation
Forecast Probabilities:
33%, 40%, 50%, 60%
Methodology:
Polygon creation, snapping configuration, attribute assignment, geoprocessing intersection, rule-based styling
Year:
2021
Region / City:
Global
Topic:
Precipitation measurement, Ground clutter, Radar data
Document Type:
Research Paper
Organization / Institution:
NASA GSFC, SSAI, UMBC
Authors:
Mircea Grecu, David T. Bolvin, Gerald Heymsfield, Stephen Lang, Stephen Nicholls, William S. Olson
Target Audience:
Researchers in meteorology, Remote sensing professionals
Period of Validity:
Ongoing research
Approval Date:
N/A
Modification Date:
N/A
Note:
Contextual Description
Year:
2023
Region / city:
Australia
Topic:
Precipitation probability of exceedance
Document type:
Research Report
Institution:
Bureau of Meteorology
Author:
Bureau of Meteorology Research Team
Target audience:
Agricultural producers, planning authorities
Period of validity:
Not specified
Approval date:
Not specified
Date of changes:
Not specified
Year:
2026
Region:
Tropical regions, global
Topic:
Climate science, atmospheric circulation, precipitation
Document type:
Research article
Institution:
University or research institution (implied from authorship)
Authors:
Aaron Donohoe, Aiko Voigt
Target audience:
Climate scientists, meteorologists, environmental researchers
Time period analyzed:
Seasonal to multi-decadal climate patterns
Date of publication:
2026
Methodology:
Analysis of ITCZ shifts, hemispheric energy contrasts, climate model simulations
Key phenomena:
Intertropical Convergence Zone (ITCZ), Hadley circulation, tropical precipitation patterns
Climate forcings:
Anthropogenic CO₂ increase, paleoclimatic variability
Spatial focus:
Pacific, Indian monsoon regions, South-East Asia, Australia, Northern Africa
Data type:
Climatological observations, model simulations
Note:
Year
Organization / Institution:
Toro Company, Irrigation Division
Year:
2025
Region / City:
California
Theme:
Energy, Forecasting, Grid Operations, Renewable Energy
Document Type:
Grant Funding Opportunity
Organization:
California Energy Commission
Author:
California Energy Commission
Target Audience:
Researchers, Utilities, Energy Market Participants, Policy Makers
Period of Validity:
October 2025 onwards
Approval Date:
October 2025
Date of Modifications:
N/A
Year:
2023
Region / City:
Not specified
Topic:
Demand forecasting, inventory management, machine learning, supply chain optimization
Document Type:
Review paper
Author:
Not specified
Target Audience:
Researchers, supply chain practitioners
Period of Action:
Not specified
Approval Date:
Not specified
Date of Changes:
Not specified
Year:
2024-2025
Region / City:
Florida
Subject:
Transportation forecasting, travel demand modeling
Document Type:
Work Plan
Organization / Institution:
Florida Department of Transportation (FDOT)
Author:
Florida Transportation Forecasting Forum (TFF)
Target Audience:
Transportation planners, engineers, decision-makers, modeling professionals
Period of Validity:
2024-2025
Approval Date:
Not specified
Date of Changes:
Not specified
Year:
2024
Region / City:
Nigeria
Topic:
Hydrology, Aquifers, Marine Science
Document Type:
Report
Author:
Not specified
Target Audience:
Researchers, Hydrologists, Marine Scientists
Period of Validity:
June 2024
Approval Date:
Not specified
Modification Date:
Not specified
Year:
Not specified
Region / City:
Not specified
Topic:
Weather forecasting, meteorology
Document Type:
Lab report
Organization / Institution:
University of Illinois
Author:
Not specified
Target Audience:
Students studying meteorology
Period of validity:
Not specified
Approval Date:
Not specified
Modification Date:
Not specified
Year:
2022
Region / City:
United States
Topic:
Climate-Informed Forecasting, Epidemiology, West Nile Virus
Document Type:
Scientific Research
Institution:
R Core Team, Goodrich et al.
Author:
Not specified
Target Audience:
Researchers, epidemiologists, climate scientists
Period of Validity:
2022
Date of Approval:
Not specified
Date of Changes:
Not specified
Year:
N/A
Region / City:
N/A
Theme:
Forecasting Techniques
Document Type:
Exam Questions
Organization / Institution:
N/A
Author:
Scott Stevens
Target Audience:
Students
Period of Action:
N/A
Approval Date:
N/A
Date of Changes:
N/A
Chapter:
12
Subject:
Forecasting
Discipline:
Operations Management
Type of document:
Educational textbook exercises with solutions
Topics covered:
Moving average; Exponential smoothing; Adjusted exponential smoothing; Seasonal forecasting; Linear trend line; Linear regression; Multiple regression; Forecast accuracy measures; Control charts
Statistical measures:
MAD; Cumulative error (E); Average error (Ē); Correlation coefficient; Coefficient of determination
Organizations mentioned:
Tech; Bee Line Café; ITown; State University
Time references:
Eight semesters; Twelve quarters; Ten years; Forecast for 2005; Forecast for year 11
Software referenced:
Excel
Intended audience:
Students of operations management
Year:
2019
Region / City:
Australia
Topic:
Tobacco Excise
Document Type:
Working Paper
Organization:
The Treasury
Author:
Jonathan O’Bannon, John Clark
Target Audience:
Government policy makers, economists
Period of Validity:
Ongoing
Approval Date:
June 2019
Date of Modifications:
N/A
Year:
2023
Region / City:
Glassboro, NJ, USA
Theme:
Environmental Impact, Life Cycle Assessment, Machine Learning
Document Type:
Research Article
Organization / Institution:
Rowan University
Author:
Harriet Dufie Appiah, Matthew Conway, Jahnvi Patel, Marcella McMahon, Robert Hesketh, Kirti M. Yenkie
Target Audience:
Researchers, Environmental Scientists, Engineers
Period of Action:
Not specified
Approval Date:
Not specified
Date of Changes:
Not specified
Keywords:
Life Cycle Assessment, Machine Learning, Environmental Impact, Emissions
Contextual Description:
A research article discussing the application of machine learning models to predict environmental impacts of chemicals at the early design stage based on their molecular properties and life cycle data.
Year:
2023
Region / City:
Global
Topic:
Weather Forecasting, Machine Learning
Document Type:
Research Paper
Organization / Institution:
Not specified
Author:
Not specified
Target Audience:
Researchers, Meteorologists, Machine Learning Practitioners
Period of Validity:
Not specified
Approval Date:
Not specified
Date of Changes:
Not specified
Keywords:
Weather forecasting, Regression Techniques, Prediction, MSE
Context:
The document presents a research study on applying machine learning regression techniques for real-time weather forecasting and prediction.
Year:
2023
Region / City:
Aurangabad District, Maharashtra
Subject:
Potential Evapotranspiration Forecasting
Document Type:
Research Article
Author:
Not specified
Target Audience:
Researchers, Hydrologists, Water Resource Managers
Period of Study:
1970–2023
Date of Approval:
Not specified
Date of Modifications:
Not specified
Keywords:
PET Forecasting, ANN, Thornthwaite Method, Water Resource Management
Year:
1998
Region / City:
Global
Subject:
Demand Forecasting
Document Type:
Educational Text
Institution:
Not specified
Author:
Not specified
Target Audience:
Students, Business Professionals
Period of Validity:
Not specified
Approval Date:
Not specified
Modification Date:
Not specified
Year:
2023
Region / City:
Aurangabad, Marathwada region, Maharashtra
Theme:
Water resource management, Evapotranspiration forecasting, Agricultural planning
Document type:
Research article
Organization / Institution:
Not specified
Author:
Not specified
Target Audience:
Researchers, water resource managers, agricultural planners
Period of validity:
1970-2023
Approval date:
Not specified
Date of modifications:
Not specified
Methodology:
Thornthwaite method, Artificial Neural Network (ANN)
Keywords:
Potential Evapotranspiration, Artificial Neural Network, Thornthwaite method, Time series analysis, Hydrological forecasting
Note:
Contextual description