# Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data
## 1 Introduction  
Meteorological monitoring plays an important role in weather forecasting [1], disaster prediction [2], traffic flow prediction [3] and agriculture [4; 5; 6]. Since climate change has come to be recognized as a serious issue over recent years, meteorological monitoring has gained in importance. Weather observation systems are installed in many locations throughout the world. The Automated Meteorological Data Acquisition System (AMeDAS) developed by the Japan Meteorological Agency has been installed at 1300 locations in Japan. AMeDAS has sensors to monitor air temperature, humidity, solar radiation/duration, wind speed/direction, rainfall, atmospheric pressure and other factors. Since the accuracy of the data is certified by the Japan Meteorological Business Support Center, data obtained by AMeDAS are used for many purposes including weather forecasting, disaster prediction and crop modeling [7; 8; 9]. However, such weather observation systems are expensive, and installation is complex. This makes the system challenging to install in many places or other places where measurement is most critical. To address these problems of cost and flexibility, wireless sensor networks (WSNs) have been developed [10; 11; 12; 13; 14; 15; 16; 17]. Since one of the objectives of WSNs is cost reduction, low-cost sensors are often used. However, there is a tradeoff between the accuracy and cost of sensors [18], and the reliability ofdata acquired by WSNs is often a matter of dispute [19; 20; 21]. The accuracy of low-cost sensors used for air temperature measurement has been widely studied in agricultural research [19; 20; 21; 22; 23; 24; 25; 26; 27], because air temperature is an important factor in plant growth. Air temperature measurements are influenced by solar radiation, humidity, wind speed, rainfall and the azimuth [19; 22; 23; 26; 28]. The effect of solar radiation is the most significant of these. High-accuracy air temperature sensors are therefore covered with radiation shielding made from material with a high heat capacity. Forced aspiration systems are also used to avoid condensation in the cover and to measure the air temperature outside the cover. The air temperature sensors used in AMeDAS (Figure 1) have a forced aspiration system that generates an air speed of 5 ms\({}^{-1}\). However, these additions make the sensors much more expensive. The forced aspiration system also requires an electric power source, which is often challenging at outdoor sites. To address this, studies have been conducted with software-based calibration methods for low-cost air temperature sensors. [PERSON] et al. [20] investigated air temperature error correction. They developed a look-up table of solar radiation and the resulting difference in air temperature measured by low-cost and high-accuracy sensors, and they used this to conduct the error correction. They also considered the time lag of temperature measured by low-cost and high-accuracy sensors, caused by the time taken to transmit energy from solar radiation to the atmosphere. [PERSON] et al. [24] investigated the improvement of air temperature measurement by low-cost sensors, using daytime data, which are influenced by the time of sunrise and sunset. The data were preprocessed using discrete wavelet transform to remove noise. They then established a model based on a back propagation neural network, in which the input and output data were air temperature measurements from low-cost sensors and the national ground weather station respectively. Although these previous studies have improved the accuracy of air temperature measurement by low-cost sensors, there remains considerable room for improvement. First, the data used had very limited variation. [PERSON] et al. [20] used 14 days of data recorded from June to December in the same year, and [PERSON] et al. [24] used the data from 10 cloudy days in June and July of the same year. Seasonal factors are especially critical in Japan, which has four seasons with very different climatic conditions.