Browse > Article
http://dx.doi.org/10.5532/KJAFM.2020.22.1.1

Prediction of Soil Moisture with Open Source Weather Data and Machine Learning Algorithms  

Jang, Young-bin (Program in Regional Information, Department of Agricultural Economics and Rural Development, College of Agriculture and Life Sciences, Seoul National University)
Jang, Ik-hoon (Program in Regional Information, Department of Agricultural Economics and Rural Development, College of Agriculture and Life Sciences, Seoul National University)
Choe, Young-chan (Program in Regional Information, Department of Agricultural Economics and Rural Development, College of Agriculture and Life Sciences, Seoul National University)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.22, no.1, 2020 , pp. 1-12 More about this Journal
Abstract
As one of the essential resources in the agricultural process, soil moisture has been carefully managed by predicting future changes and deficits. In recent years, statistics and machine learning based approach to predict soil moisture has been preferred in academia for its generalizability and ease of use in the field. However, little is known that machine learning based soil moisture prediction is applicable in the situation of South Korea. In this sense, this paper aims to examine 1) whether publicly available weather data generated in South Korea has sufficient quality to predict soil moisture, 2) which machine learning algorithm would perform best in the situation of South Korea, and 3) whether a single machine learning model could be generally applicable in various regions. We used various machine learning methods such as Support Vector Machines (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting Machines (GBM), and Deep Feedforward Network (DFN) to predict future soil moisture in Andong, Boseong, Cheolwon, Suncheon region with open source weather data. As a result, GBM model showed the lowest prediction error in every data set we used (R squared: 0.96, RMSE: 1.8). Furthermore, GBM showed the lowest variance of prediction error between regions which indicates it has the highest generalizability.
Keywords
Soil moisture prediction; Machine learning; Agricultural big data; Gradient boosting machines; Publicly available data in agriculture;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Food and Agriculture Organization of the United Nations, Rome, 1-15.
2 Breiman, L, 2001: Random forests. Machine learning 45(1), 5-32.   DOI
3 Cai, Y., W. Zheng, X. Zhang, L. Zhangzhong, and X. Xue, 2019: Research on soil moisture prediction model based on deep learning. PloS One 14(4).
4 Choi, K. M., S. H. Kim, M. Son, and J. Kim, 2008: Soil moisture modelling at the mopsoil of a hillslope in the Gwangneung National Arboretum using a transfer function. Korean Journal of Agricultural and Forest Meteorology 10(2), 35-46. (in Korean with English abstract)   DOI
5 Choi, S. W., S. J. Lee, J. Kim, B. L. Lee, K. R. Kim, and B. C. Choi, 2015: Agrometeorological observation environment and periodic report of korea meteorological administration: current status and suggestions. Korean Journal of Agricultural and Forest Meteorology 17(2), 144-155. (in Korean with English abstract)   DOI
6 Cisty, M., F. Cyprich, and V. Soldanova, 2018: Prediction of soil moisture data by various regression techniques. Proceedings of International Multidisciplinary Scientific GeoConference, Surveying Geology and mining Ecology Management, Sofia, 383-389.
7 Drucker, H., C. J. Burges, L. Kaufman, A. J. Smola, and V. Vapnik, 1997: Support vector regression machines. Advances in Neural Information Processing Systems 9, 155-161.
8 Friedman, J. H., 2001: Greedy function approximation: a gradient boosting machine. Annals of Statistics 29(5), 1189-1232.   DOI
9 Geurts, P., D. Ernst, and L. Wehenkel, 2006: Extremely randomized trees. Machine Learning 63(1), 3-42.   DOI
10 Gill, M. K., T. Asefa, M. W. Kemblowski, and M. McKee, 2006: Soil moisture prediction using support vector machines. Journal of the American Water Resources Association 42(4), 1033-1046.   DOI
11 Goodfellow, I., Y. Bengio, and A. Courville, 2016: Deep Learning. MIT press, 1-26.
12 He, K., X. Zhang, S. Ren, and J. Sun, 2015: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision, Institute of Electrical and Electronics Engineers, Santiago, 1026-1034.
13 https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=72 (2019. 12. 09)
14 https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36 (2019. 12. 09)
15 Natekin, A., and A. Knoll, 2013: Gradient boosting machines, a tutorial. Frontiers in Neurorobotics 7, 21pp.   DOI
16 Kingma, D. P., and J. Ba, 2014: Adam: a Method for Stochastic Optimization. Proceedings of Third International Conference for Learning Representations, San Diego.
17 Kohavi, R., 1995: A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai 14(2), 1137-1145.
18 Laio, F., A. Porporato, L. Ridolfi, and I. Rodriguez-Iturbe, 2001: Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress: II. Probabilistic soil moisture dynamics. Advances in Water Resources 24(7), 707-723.   DOI
19 National Center for Atmospheric Research, 2004: Community Land Model version 3.0 (CLM3. 0) developer's guide. U. S. Department of Energy.
20 National Weather Service, 1976: Catchment modeling and initial parameter estimation for the National Weather Service river forecast system. Office of Hydrology.
21 Nielsen, D., 2016: Tree boosting with XGBoost-why does XGBoost win "every" machine learning competition? NTNU Norwegian University of Science and Technology.
22 Oleson, K. W., Y. Dai, G. Bonan, M. Bosilovich, R. Dickinson, P. Dirmeyer, F. Hoffman, P. Houser, G. Y. Niu, P. Thornton, M. Vertenstein, Z. L. Yang, and X. Zeng, 2004: Technical description of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-461+STR.
23 Pavlenko, T, 2003: On feature selection, curse-ofdimensionality and error probability in discriminant analysis. Journal of Statistical Planning and Inference 115(2), 565-584.   DOI
24 Van Dam, J. C., J. Huygen, J. G. Wesseling, R. A. Feddes, P. Kabat, P. E. V. Van Walsum, P. Groenendijk, and C. A. Van Diepen, 1997: Theory of SWAP version 2.0; Simulation of water flow, solute transport and plant growth in the soil-wateratmosphere-plant environment, TD45.HM/10.97, DLO Winand Staring Centre, Wageningen.
25 Prakash, S., A. Sharma, and S. S. Sahu, 2018: Soil Moisture Prediction Using Machine Learning. Proceedings of 2018 Second International Conference on Inventive Communication and Computational Technologies, Coimbatore, Institue of Electrical and Electronics Engineers, 1-6.
26 Shin, Y., B. P. Mohanty, and A. V. Ines, 2018: Development of non-parametric evolutionary algorithm for predicting soil moisture dynamics. Journal of Hydrology 564, 208-221.   DOI
27 Song, J., D. Wang, N. Liu, L. Cheng, L. Du, and K. Zhang, 2008: Soil moisture prediction with feature selection using a neural network. Proceedings of 2008 Digital Image Computing: Techniques and Applications, Canberra, Institue of Electrical and Electronics Engineers, 130-136.
28 Vapnik, V., S. E. Golowich, and A. J. Smola, 1997: Support vector method for function approximation, regression estimation and signal processing. Advances in neural information processing systems 9, 281-287.