scholarly journals A novel weather parameters prediction scheme and their effects on crops

Author(s):  
Naveen Lingaraju ◽  
Hosaagrahara Savalegowda Mohan

Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.

2020 ◽  
Vol 17 (4) ◽  
pp. 15-31
Author(s):  
Lavanya K. ◽  
Sathyan Venkatanarayanan ◽  
Anay Anand Bhoraskar

Weather forecasting is one of the biggest challenges that modern science is still contending with. The advent of high-power computing, technical advancement of data storage devices, and incumbent reduction in the storage cost have accelerated data collection to turmoil. In this background, many artificial intelligence techniques have been developed and opened interesting window of opportunity in hitherto difficult areas. India is on the cusp of a major technology overhaul with millions of people's data availability who were earlier unconnected with the internet. The country needs to fast forward the innovative use of available data. The proposed model endeavors to forecast temperature, precipitation, and other vital information for usability in the agrarian sector. This project intends to develop a robust weather forecast model that learns automatically from the daily feed of weather data that is input through a third-party API source. The weather feed is sourced from openweathermap, an online service that provides weather data, and is streamed into the forecast model through Kafka components. The LSTM neural network used by the forecast model is designed to continuously learn from predictions and perform actual analysis. The model can be architected to be implemented across very large applications having the capability to process large volumes of streamed or stored data.


Author(s):  
Atul Kulkarni ◽  
Debajyoti Mukhopadhyay

<p>Weather forecasting is a significant function in meteorology and has been one of the most systematically challenging troubles around the world.This scheme deals with the structure of a weather display method using small cost components so that any electronics hobbyist can construct it. As a replacement for using sensors to collect the weather data, the development gets the information from weather stations placed around the world through a global weather data supplier. Severe weather phenomena challengedifficult weather forecast approach with the partial explanation. Weather events have numerous parameters that are not possible to detail and compute. Growing on communication methods enables weather predictsspecialist systems to combine and share possessions and thus hybrid systems have emerged. Still, though these improvements on climate predict, these expert systems can’t be entirely reliable while weather forecast is central problem.</p>


1995 ◽  
Vol 4 (3) ◽  
pp. 141-153 ◽  
Author(s):  
Lex Wolters ◽  
Gerard Cats ◽  
Nils Gustafsson

In this article we describe the implementation of a numerical weather forecast model on a massively parallel computer system. This model is a production code used for routine weather forecasting at the meteorological institutes of several European countries. The modifications needed to achieve a data-parallel version of this model without explicit message passing are outlined. The achieved performance of different numerical solution methods within this model is presented and compared.


MAUSAM ◽  
2021 ◽  
Vol 65 (4) ◽  
pp. 509-520
Author(s):  
A.K. SHUKLA ◽  
Y.A. GARDE ◽  
INA JAIN

The present study is undertaken to develop area specific weather forecasting models based on time series data for Pantnagar, Uttarakhand. The study was carried out by using time series secondary monthly weather data of 27 years (from 1981-82 to 2007-08). The trend analysis of weather parameters was done by Mann-Kendall test statistics. The methodologies adopted to forecast weather parameters were the winter’s exponential smoothing model and Seasonal Autoregressive Integrated Moving Average (SARIMA). Comparative study has been carried out by using forecast error percentage and mean square error. The study showed that knowledge of this trend is likely to be helpful in planning and production of enterprises/crops. The study of forecast models revealed that SARIMA model is the most efficient model for forecasting of monthly maximum temperature, monthly minimum temperature and monthly humidity I. The Winter’s model was found to be the most efficient model for forecasting Monthly Humidity II but no model was found to be appropriate to forecast monthly total rainfall.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Rashmi Bhardwaj ◽  
Varsha Duhoon

The objective of the paper is to compare hybrid conjunction models with conventional models for the reduction of errors in weather forecasting. Besides the simple models like RBF model, SMO model, and LibSVM model, different hybrid conjunction models have been used for forecasting under different schemes. The forecasts from these models are further compared on the basis of errors calculated and time taken by the hybrid models and simple models in order to forecast weather parameters. In this paper, conjunction models over the convectional models are designed for forecasting the weather parameters for the reduction of error. India is a tropical country with variations in weather conditions. The objective is to build a conjunction model with less error to forecast weather parameters. A hybrid conjunction model is developed and analysed for different weather parameters for different metropolitan cities of India. Performance measurement is analysed for weather parameters. It is observed that, on the basis of error comparison and time taken by the models, the hybrid wavelet-neuro-RBF model gives better results as compared to the other models due to lower values of determined errors, better performance, and lesser time taken. The study becomes significant as weather forecasting with accuracy is a complex task along with the reduction of prediction error by the application of different models and schemes. It is concluded that the proposed hybrid model is helpful for forecasting and making policies in advance for the betterment of the human being, farmers, tourists, and so on as in all these activities, weather forecast plays an important role.


2010 ◽  
Vol 51 (54) ◽  
pp. 14-18 ◽  
Author(s):  
K. Srinivasan ◽  
Ajay Kumar ◽  
Jyoti Verma ◽  
Ashwagosha Ganju

AbstractIn this study, we use MM5 weather-forecast model output and observed surface weather data from 11 stations in the western Himalaya to develop a statistical downscaling model (SDM) to better predict precipitation, 10 m wind speed and 2 m temperature. The analysis covers three consecutive winters: 2004/05, 2005/06 and 2006/07. The performance of the SDM was assessed using an independent dataset from the 2007/08 winter season. This assessment shows that the SDM technique substantially improves the forecast over specific station locations, which is important for avalanche-threat assessment.


Author(s):  
Abdulrahman Khamaj ◽  
Amin G. Alhashim ◽  
Vincent T. Ybarra ◽  
Azham Hussain

AbstractCommunicating weather forecasts from the public perspective is essential for meeting people’s needs and enhancing their overall experiences. Due to the lack of cited work on the public’s behavior and perception of weather data and delivery sources in Middle Eastern countries such as Saudi Arabia (KSA), this study employs a cross-sectional questionnaire to fill the gap and apply the Protective Action Decision Model to non-Western individuals. The questionnaire examined respondents’ opinions about 1) the importance of weather forecast accessibility, 2) crucial weather features, and 3) available features on existing smartphone weather applications (apps) in KSA. The results showed that nearly all participants reported that their decisions of daily lives and activities were highly dependent on weather forecasts. Most participants thought weather forecast features are necessary. Though the most commonly used source for weather forecasts in KSA was smartphone apps, many participants responded that these apps were lacking specific weather functionalities (e.g., giving weather alerts to their exact location). Regression analyses found that KSA individuals who do not believe that weather forecasts are important are predicted by 1) not wanting any new features added to weather applications and 2) that weather forecasts do not impact lives nor property. This study’s findings can guide governmental and private weather agencies in KSA and other Middle Eastern or developing countries to better understand how to meet and communicate people’s weather needs.


Jurnal MIPA ◽  
2013 ◽  
Vol 2 (2) ◽  
pp. 140
Author(s):  
Ricky Daniel Aror ◽  
Hesky S. Kolibu ◽  
Guntur Pasau

Metode prakiraan cuaca yang selama ini hanya dilakukan secara manual dengan melihat parameter-parameter cuaca yang ada, sehingga dibuat suatu program aplikasi komputer untuk memudahkan para prakirawan dalam menganalisa dan membuat prakiraan cuaca. Software pembuat program, Visual Basic 6.0 dimanfaatkan dalam pembuatan program aplikasi prakiraan cuaca ini. Tersedianya form pada Visual Basic yang mudah untuk dimodifikasi, memudahkan pengguna untuk mengatur tampilan aplikasi kemudian dijalankan dengan menggunakan kode (script) sebagai perintah. Hasilnya berupa sebuah program aplikasi prakiraan cuaca berekstensi *.exe yang dapat dijalankan pada perangkat komputer. Dengan menggunakan aplikasi prakiraan cuaca ini para prakirawan akan lebih mudah menganalisis dan mengoreksi langsung pada aplikasi ini jika ada kesalahan dan waktu yang dibutuhkan relatif lebih cepat (efisien).Methods of weather forecasting have only done manually by using provided weather parameters, so there is a need to build ​​a computer application program in facilitating the forecasters to analyze and make forecasts. Maker software program called Visual Basic 6.0 application program was utilized in building this forecasts application. Availability of the Visual Basic form that is easy to modify allows users to adjust the display and then run the application using the code (script) as a command. The result is a weather forecast application program with extension *.exe that can be run on the computer. By using this application the weather forecasters will be easier to analyze and correct it directly on the application if there is an error and the time it takes relatively more quickly (efficiently).


Author(s):  
Simeon J. Yates ◽  
Jordana Blejmar

Two workshops were part of the final steps in the Economic and Social Research Council (ESRC) commissioned Ways of Being in a Digital Age project that is the basis for this Handbook. The ESRC project team coordinated one with the UK Defence Science and Technology Laboratory (ESRC-DSTL) Workshop, “The automation of future roles”; and one with the US National Science Foundation (ESRC-NSF) Workshop, “Changing work, changing lives in the new technological world.” Both workshops sought to explore the key future social science research questions arising for ever greater levels of automation, use of artificial intelligence, and the augmentation of human activity. Participants represented a wide range of disciplinary, professional, government, and nonprofit expertise. This chapter summarizes the separate and then integrated results. First, it summarizes the central social and economic context, the method and project context, and some basic definitional issues. It then identifies 11 priority areas needing further research work that emerged from the intense interactions, discussions, debates, clustering analyses, and integration activities during and after the two workshops. Throughout, it summarizes how subcategories of issues within each cluster relate to central issues (e.g., from users to global to methods) and levels of impacts (from wider social to community and organizational to individual experiences and understandings). Subsections briefly describe each of these 11 areas and their cross-cutting issues and levels. Finally, it provides a detailed Appendix of all the areas, subareas, and their specific questions.


2021 ◽  
Vol 11 (3) ◽  
pp. 1062
Author(s):  
Krzysztof Koszela ◽  
Wojciech Mueller ◽  
Jakub Otrząsek ◽  
Mateusz Łukomski ◽  
Sebastian Kujawa

The paper concentrates on researching the possibilities of using modern information technologies in animal production in order to monitor and identify behavior and well-being of cows. Having in mind the challenges related to managing dairy herds, and economic pressure put on breeders (as well as the broadly defined well-being of animals), an endeavor was made to create a new method, which would be competitive in comparison with the existing solutions. The proposed method of collecting data and data processing with beacon devices as well as data warehouse, allows—according to the authors—a more complete identification of behaviors and physiological condition of a dairy herd. It is also worth pointing out that this method is competitive in terms of price. By virtue of the multitude of data that were collected, a decision was made to resign from processing data on a local computer and use a cloud compute engine instead. The presented information system creates a sequence of components, which were subject to verification both on the level of creating and conducting research. Research results that were received were then compared with knowledge presented in the literature. A vital element of validation of the aforementioned methodology was comparing results that were achieved in the course of research work with the system making use of pedometer. The aim of the authors was to develop a new information technology solution, as well as a method based on beacons, which are rather universal devices, with the use of data warehouses, allowing the identification of behavior and physiological state of milk cattle, the method which would be competitive in comparison with the existing solutions, especially in terms of price. In the proposed solution, both information coming from microcomputers and weather forecast data coming from weather forecast stations, which make the above identification easy, were used as data sources.


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