A novel weather parameters prediction scheme and their effects on crops

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.

Short range SW monsoon rainfall forecasting over India using neural networks

Feedforward Neural Networks are used for daily precipitation forecast using several test stations all over India. The six year European Centre of Medium Range Weather Forecasting (ECMWF) data is used with the training set consisting of the four year data from 1985-1988 and validation set consisting of the data from 1989-1990. Neural networks are used to develop a concurrent relationship between precipitation and other atmospheric variables. No attempt is made to select optimal variables for this study and the inputs are chosen to be same as the ones obtained earlier at National Center for Medium Range Weather Forecasting (NCMRWF) in developing a linear regression model. Neural networks are found to yield results which are atleast as good as linear regression and in several cases yield 10 - 20 % improvement. This is encouraging since the variable selection has so far been optimized for linear regression.

A scheme to detect sand/dust weather applying meteorological radars

Sandy dust weather occur frequently in arid and semi-arid areas. It is important to actually detect the sandy dust grain concentration or the visibility of the sandy dust weather for weather forecasting. In this paper, based on numerical calculation of the effective detection distance of different radar detecting the sandy-dust weather with different strength, a scheme to detect sand/dust weather applying existed meteorological radar stations is proposed in this paper. The scheme can be efficient to detect sandy dust weather, for it makes a good supplement to the current deficiencies in detecting sandy dust weather and it’s a cost-saving detection way by using the existed meteorological radars. In addition, the effect of charges carried by sand/dust grains and the relative humidity on the effective detection distance of radar is also investigated, and it shows that these effects will not change the proposed scheme. It will be promising to detect the sandy dust weather in the way of disastrous weather precaution by using this scheme.

AIEADA 1.0: Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction, and is a crucial building block that has allowed dramatic improvements in weather forecasting over the past few decades. DA is commonly framed in a variational setting, where one solves an optimization problem within a Bayesian formulation using raw model forecasts as a prior, and observations as likelihood. This leads to a DA objective function that needs to be minimized, where the decision variables are the initial conditions specified to the model. In traditional DA, the forward model is numerically and computationally expensive. Here we replace the forward model with a low-dimensional, data-driven, and differentiable emulator. Consequently, gradients of our DA objective function with respect to the decision variables are obtained rapidly via automatic differentiation. We demonstrate our approach by performing an emulator-assisted DA forecast of geopotential height. Our results indicate that emulator-assisted DA is faster than traditional equation-based DA forecasts by four orders of magnitude, allowing computations to be performed on a workstation rather than a dedicated high-performance computer. In addition, we describe accuracy benefits of emulator-assisted DA when compared to simply using the emulator for forecasting (i.e., without DA). Our overall formulation is denoted AIAEDA (Artificial Intelligence Emulator Assisted Data Assimilation).

The AI Forecaster: Machine Learning Takes on Weather Prediction

A novel approach to weather forecasting uses convolutional neural networks to generate exceptionally fast global forecasts based on past weather data.

Literature Survey for Applications of Artificial Neural Networks

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

Automatic Remote Farm Irrigation System with WSN and Weather Forecasting

The paper showcases the system used for automating agriculture using wireless sensor network (WSN) and weather prediction. WSN, is more efficient than IoT as it avoids connecting all the sensor nodes directly to Internet, thus reducing the traffic over Internet and energy consumption of the sensor network. The system consists of a clustered tree topology to increase the range of operation, connectivity and easily connect new nodes dynamically. The sensor nodes being the leaves, local gateways being the branches and the global gateway being the root node. The system is implemented using cost effective micro-controllers, robust communication modules and reliable data showcasing platforms. Our implementation uses weather prediction to minimize the water needed for irrigation. Thereby minimizing cost and increasing efficient usage of resources.

Essentials, Challenges, and Future Directions of Agricultural IoT

Agriculture plays a major role in the socio-economic structure of India. A recent report claimed that population of India is increasing faster than its capability to produce rice, wheat, and vegetables. The challenges in the area of agriculture are farming, watering, weather forecasting, marketing, and transportation. These challenges are to be addressed towards proper solution. If the infrastructure and productivity of the food increases, then India can easily feed its population as well as improve the exports of wheat and rice around the world. Internet of things (IoT) is an emerging technical area of agriculture domain. The advantage of IoT is to implement a smart agriculture management system with the help of analyzing the weather conditions of the field in order to optimize the usage of water, energy, fertilizers so as to maximize the crop yield. The objective of this study is to explore the possible contributions of IoT in Indian agriculture towards the improvements in irrigation infrastructure, agricultural productivity, food security, and rural job opportunities.

Classi-Fly: Inferring Aircraft Categories from Open Data

In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of intelligence about military and government movements. Typically, these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category. armasuisse Science + Technology , the R&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than 2 million aircraft. We show that we can obtain the correct aircraft category with an accuracy of greater than 88%. In cases, where no metadata is available, this approach can be used to create the data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect particular sensitive aircraft such as police and surveillance aircraft using this method.