Micro-climate Prediction – Multi Scale Encoder-decoder based Deep Learning Framework

This paper presents a deep learning approach for a versatile Micro-climate prediction framework (DeepMC). Micro climate predictions are of critical importance across various applications, such as Agriculture, Forestry, Energy, Search & Rescue, etc. To the best of our knowledge, there is no other single framework which can accurately predict various micro-climate entities using Internet of Things (IoT) data. We present a generic framework (DeepMC) which predicts various climatic parameters such as soil moisture, humidity, wind speed, radiation, temperature based on the requirement over a period of 12 hours – 120 hours with a varying resolution of 1 hour – 6 hours, respectively. This framework proposes the following new ideas: 1) Localization of weather forecast to IoT sensors by fusing weather station forecasts with the decomposition of IoT data at multiple scales and 2) A multi-scale encoder and two levels of attention mechanisms which learns a latent representation of the interaction between various resolutions of the IoT sensor data and weather station forecasts. We present multiple real-world agricultural and energy scenarios, and report results with uncertainty estimates from the live deployment of DeepMC, which demonstrate that DeepMC outperforms various baseline methods and reports 90%+ accuracy with tight error bounds.