Accurate weather forecasting is a functional prerequisite for global industries including disaster management, agriculture, air travel, and retail. Traditional weather forecasting practices relied on human intelligence to infer complex statistical data including satellite imagery, precipitation reports, etc. However, human lethargy and lack of accurate data analysis have led business leaders to explore artificial intelligence services for this sector. Forecasters believe that AI weather forecasting can save significant prediction time that directly influences business efficiency and growth in the long run.
This blog post explores the long-term business benefits and use cases of AI-powered weather forecasting across industries.
The agriculture industry suffers million-dollar losses every year due to poor environmental conditions and unreliable manual weather predictions. Inefficient use of agricultural data and remote internet accessibility prevent farmers, food companies, and financiers to meet growing consumer demands.
According to IBM, weather events cause 90% of crop losses and 25% of these losses can be prevented by using predictive weather modeling.
Artificial intelligence combined with IoT devices and predictive analytics can potentially overcome major agriculture challenges in the following ways-
a) The machine learning algorithms behind predictive analytics use satellite imageries, soil data, and hyper-local weather reports to provide irrigation insights. AI weather predictions inform farmers about optimal sowing periods, pest infestation, and changes in rain patterns to yield quality crops consistently.
b) Also, AI weather forecasting uses terrestrial data to predict appropriate transportation conditions to facilitate timely crop delivery and avoid wastage.
The AI team at Oodles is skilled at training and building industry-specific machine learning models to boost business performances. We work with big data technologies such as Hadoop and Spark to optimize agriculture businesses in the following ways-
a) We thoroughly process and cleanse raw Electronic Data Recorder’s Data to apply the logic to analyze different environmental conditions. By training the model with the prepared dataset, we can make accurate predictions about motor stall conditions, soil fertility, temperature, etc.
b) We build custom predictive analysis engines to anticipate your business outcomes and user behavior based on historical data and market trends.
Weather conditions are one of the strongest factors that influence retail consumer demands. An exponential increase in product and consumer variations has led businesses to explore smarter ways of identifying consumer needs.
With artificial intelligence, companies can no map realtime weather conditions of marketable areas to manage demand and reduce wastage. For instance, AI at retail stores can collect seasonal consumer data and extract insights to adjust product positioning accordingly.
Here are some more ways in which AI weather forecasting can boost retail businesses-
a) AI-powered predictive engines can analyze the purchasing patterns of consumers located in different weather conditions to streamline seasonal advertisement efforts.
b) The food industry can prevent tonnes of food wastage by deploying AI to detect and balance uneven consumption due to weather fluctuations.
We, at Oodles AI, use the power of predictive models to enable retail businesses to make informed and result-oriented product-related decisions. Our team can build and deploy AI weather forecasting models to boost retail businesses in the following manner-
a) We use machine-learning capabilities to combine recommendations and predictive engines to provide a customized shopping experience and services.
b) Our cloud-based predictive analytics services are effective at scaling and strengthening eCommerce business processes and applications.
Talk to our Machine Learning Development team to know more about our diverse Artificial Intelligence Services.