Hakkı Fırat ALTINBİLEK, Hakan NAR, Sefa AKSU, Ünal KIZIL

Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees

Meteorology stations sold in the market have various difficulties in terms of their use, also these systems are costly to obtain. With state of the art sensor technologies, the development of mini weather stations has become easier. This study focuses on the development of a model weather station device using temperature, relative humidity, UV, LDR Light, rain and soil moisture sensors to collect major environmental data. The measured data were wirelessly transmitted to the remote station for logging via the GSM module and the information was sent to the database in the internet environment. In addition, the data from the sensors are organized by correlation. The classification was made according to the data obtained from the rain sensor and the relationship between the other 5 sensors used in the device to the rain classification was examined. Sensor data were scaled between 0-1 with min-max normalization before being subjected to deep learning and machine learning training. In the Decision Tree (DT) a model score of 0.96 was obtained by choosing the maximum depth of 20. The artificial neural network (ANN) yielded a classification score of 0.92 using 4 hidden layers and 100 epochs in the artificial neural network model.

Keywords:

Agricultural sensors environmental control, machine learning, precipitation forecast, weather station,

PDF

___

Abyaneh, H.Z., Varkeshi, M.B., Golmohammadi, G., & Mohammadi, K. (2016). Soil temperature estimation using an artificial neural network and co-active neuro-fuzzy inference system in two different climates. Arab. J. Geosci. 9(5), 377. DOI: https://doi.org/10.1007/s12517-016-2388-8
Anitescu, C., Atroshchenko, E., Alajlan, N., & Rabczuk, T. (2019). Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, vol. 59, no.1, pp. 345–359. https://doi.org/10.32604/cmc.2019.06641
Ardiansyah, A. Y., Sarno, R., & Giandi, O. (2018). Rain detection system for estimate weather level using Mamdani fuzzy inference system. International conference on information and communications technology (pp. 848-854).Yogyakarta, Indonesia. Retrieved from: https://www.researchgate.net/publication/324962319_Rain_detection_system_for_estimate_weather_level_using_Mamdani_fuzzy_inference_system
Barik, L. (2019). IoT based temperature and humidity controlling using arduino and raspberry pi. International Journal of Advanced Computer Science and Applications, 10(9), 494-502. DOI: https://dx.doi.org/10.14569/IJACSA.2019.0100966
Bhagat, S.K., Tung, T.M., & Yaseen, Z.M. (2020). Development of artificial intelligence for modeling wastewater heavy metal removal: state of the art, application assessment and possible future re-search. J. Clean. Prod. 250, 119473. DOI: https://doi.org/10.1016/j.jclepro.2019.119473
Bochtis, D., Liakos, G.K., Busato, P., Moshou, D., & Pearson, S. (2018). Machine learning in agriculture: a review. Sensors, 18(8), 2674. DOI: https://doi.org/10.3390/s18082674
Bounsaythip, C., & Esa, R.R. (2001). Overview of data mining for customer behavior modeling. VTT Information Technology Research Report, Version:1, pp. 1-53. Retrieved from: http://www.vtt.fi/inf/julkaisut/muut/2001/customerprofiling.pdf
Cover, T. M., & Thomas, J. A. (1991). Elements of ınformation theory. New York: Wiley & Sons. Retrieved from: http://staff.ustc.edu.cn/~cgong821/Wiley.Interscience.Elements.of.Information.Theory.Jul.2006.eBook-DDU.pdf
Dongare, A.D., Kharde, R.R., & Kachare, A.D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology, 2(1), 189-194. Retrieved from: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1082.1323&rep=rep1&type=pdf
Durrani, A., Khurram, M., & Khan, H. R. (2019). Smart weather alert system for dwellers of different areas, in Proc. 16th Int. Bhurban Conf. Applied Sciences & Technology (pp.333-339). Islamabad, Pakistan. DOI: https://doi.org/10.1109/ıbcast.2019.8667190
Gotmare, V., Kolte, R., & Thengodkar, R. (2021). Weather monitoring system using arduino uno. International Engineering Journal For Research & Development, 5(5), 1-8. DOI: https://doi.org/10.17605/OSF.IO/R8XWP
Hwang, J., Orenstein, P., Cohen, J., Pfeiffer, K., & Mackey, L. (2019). Improving subseasonal forecasting in the western U.S. with machine learning. The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’19). New York, USA. DOI: https://doi.org/10.1145/3292500.3330674
Joshi, P., Mistry, N., Khan, A., Motekar, H., & Chaugule,A. (2021). Weather data accumulation using Arduino. International Journal of Innovative Research in Computer and Communication Engineering. Volume 9, Issue 4, April 2021. DOI: https://doi.org/10.15680/IJIRCCE.2021.0904041
Kızıl, Ü., Aksu, S., & Çamoğlu, G. (2018). Kontrollü ortamda bitkisel yetiştiricilik için arduino uyumlu bir toprak nemi izleme sistemi tasarımı. ÇOMÜ Ziraat Fakültesi Dergisi, 6 (2), 131-139. Retrieved from: https://dergipark.org.tr/tr/pub/comuagri/issue/41582/434661
Ko C-M, Jeong YY, Lee Y-M, & Kim B-S. (2020). The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications. Atmosphere. 2020; 11(1), 111. DOI: https://doi.org/10.3390/atmos11010111
Kumari, S., Kasliwal, M. H., & Valakunde, N. D. (2018). An android based smart environmental monitoring system using IoT. In Singh M., Gupta P., Tyagi V., Flusser J., & Ören T. (Eds.), Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-13-1813-9_53
Laskar, M. R., Bhattacharjee, R., Giri, M. S., & Bhattacharya, P. (2016). Weather Forecasting Using Arduino Based Cube-Sat. Procedia Computer Science, 89, 320–323. DOI: https://doi.org/10.1016/j.procs.2016.06.078
Mehr, A. D. (2021). Drought classification using gradient boosting decision tree. Acta Geophys. 69, 909–918. DOI: https://doi.org/10.1007/s11600-021-00584-8
Miao, Q., Pan, B., Wang, H., Hsu, K., & Sorooshian, S. (2019). Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network. Water, 11(5), 977. https://doi.org/10.3390/w11050977
Mitchell, S., Weersink, A., & Erickson, B. (2018). Adoption of precision agriculture technologies in Ontario crop production. Canadian Journal of Plant Science, 98(6), 1384-1388. DOI: https://doi.org/10.1139/cjps-2017-0342
Mohapatra, G., Rakesh, V., Purwar, S., & Dimri, A.P. (2021). et al. Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques. Theoretical and Applied Climatology, 145(1-2), 673-686. DOI: https://doi.org/10.1007/s00704-021-03644-7
Murray J., & Mackinnon, N.G. (1999). Data mining and knowledge discovery in databases- an overview. J.Statists, Vol.41, No.3, p.260. DOI: https://doi.org/10.1111/1467-842X.00081
Pal, M., & Mather, P.M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26, 1007-1011. DOI: https://doi.org/10.1080/01431160512331314083
Sirohi, K., Tanwar, A., Himanshu, Jindal, P. (2016). Automated irrigation and fire alert system based on Hargreaves equation using weather forecast and Zigbee protocol. IEEE 2nd International Conference on Communication, Control and Intelligent Systems (CCIS), Mathura, India. DOI: https://doi.org/10.1109/CCIntelS.2016.7878191
Tiyasha, Minh Tung, T., Mundher Yaseen, Z. (2020). A survey on river water quality modelling using artificial intelligence models: 2000–2020. J. Hydrol., 124670. DOI: https://doi.org/10.1016/j.jhydrol.2020.124670.

Journal of Advanced Research in Natural and Applied Sciences-Cover

Yayın Aralığı: Yılda 4 Sayı
Başlangıç: 2015
Yayıncı: Çanakkale Onsekiz Mart Üniversitesi

Arşiv