1 |
Isabona, J., Imoize, A. L. and Kim, Y., 2022, Machine learning-based boosted regression ensemble combined with hyperparameter tuning for optimal adaptive learning, Sensors, 22(10), 3776. doi: 10.3390/s22103776
DOI
|
2 |
Scikit-learn user guide 1.1.2, Available online: https://scikit-learn.org/stable/user_guide.html. (accessed on 1 May 2022).
|
3 |
Yu, C. and Chen, J., 2020, Landslide susceptibility mapping using the slope unit for Southeastern Helong City, Jilin Province, China: A comparison of ANN and SVM, Symmetry, 12(6), 1047. doi:10.3390/sym12061047
DOI
|
4 |
Xia, D., Tang, H., Sun, S., Tang, C. and Zhang, B., 2022, Landslide susceptibility mapping based on the germinal center optimization algorithm and support vector classification, Remote Sens., 14, 112707. doi:10.3390/rs14112707
DOI
|
5 |
Abdolrasol, M. G. M., Hussain, S. M. S., Ustun, T. S., Sarker, M. R., Hannan, M. A., Mohamed, R., Ali, J. A., Mekhilef, S. and Milad, A., 2021, Artificial neural networks based optimization techniques: A review, Electronics, 10, 2689. doi:10.3390/electronics10212689
DOI
|
6 |
Wysocki, M. and Slepaczuk, R., 2022, Artificial neural networks performance in WIG20 index options pricing, Entropy, 24, 35. doi: 10.3390/e24010035
DOI
|
7 |
Chicco, D., Warrens, M. J. and Jurman, G., 2021, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Comput. Sci., 7, e623. doi:10.7717/peerj-cs.623
DOI
|
8 |
Bergstra, J. and Bengio, Y., 2012, Random search for hyperparameter optimization, J. Mach. Learn. Res., 13(10): 281-305, doi:10.5555/2188385.2188395
DOI
|
9 |
Yang, L. and Shami, A., 2020, On hyperparameter optimization of machine learning algorithms: Theory and practice, Neurocomputing, 415: 295-316, doi:10.1016/j.neucom.2020.07.061
DOI
|
10 |
Rodriguez-Galiano, V. F., Ghimire, B. Rogan, J. Chica-Olmo, M. and Rigol-Sanchez, J. P., 2012, An Assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS J. Photogramm. Remote Sens., 67: 93-104. doi:10.1016/j.isprsjprs.2011.11.002
DOI
|
11 |
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M. and Chica-Rivas, M., 2015, Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines, Ore Geology Reviews, 71: 804-818. doi: 10.1016/j.oregeorev.2015.01.001
DOI
|
12 |
Stathakis, D., 2009, How many hidden layers and nodes?, International Journal of Remote Sensing, 30(8). doi:10.1080/01431160802549278
DOI
|
13 |
Petropoulos, G. P., Kontoes, C. C. and Keramitsoglou, I., 2012, Land cover mapping with emphasis to burnt area delineation using co-orbital ALI and Landsat TM imagery, Int. J. Appl. Earth. Obs. Geoinf., 18: 344-355. doi: 10.1016/j.jag.2012.02.004
DOI
|
14 |
Ponce, V. M. and Hawkins, R. H., 1996, Runoff Curve Number: Has It Reached Maturity?, J. Hhydraul. Eng., 1(1): 11-19. doi:10.1061/(ASCE)1084-0699(1996)1:1(11)
DOI
|
15 |
Sheela, K. G. and Deepa, S. N., 2013, Review on methods to fix number of hidden neurons in neural networks, Mathematical Problems in Engineering, doi:10.1155/2013/425740
DOI
|
16 |
Lv, Y., Le, Q. T. Bui, H. B. Bui, X. N. Nguyen, H. Nguyen-Thoi, T. Dou, J. and Song, Z., 2020, A comparative study of different machine learning algorithms in predicting the content of ilmenite in titanium placer, Appl. Sci., 10, doi:10.3390/app10020635
DOI
|
17 |
Nguyen, H., Bui, X. N. Bui, H. B. and Mai, N. L., 2018, A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam, Neural Comput. Appl., 32: 3939-3955. doi:10.1007/s00521-018-3717-5
DOI
|
18 |
Heaton, J., 2008, Introduction to neural networks with java: 2nd Edition, Heaton Research, Inc., ISBN:9781604390087.
|
19 |
Lee, K. H, 2007, Estimation method and improvement of agricultural water demand, Magazine of the Korean Society of Agricultural Engineers, 49(3): 4-11 (in Korean).
|
20 |
Tong, L., Kang, S. and Zhang, L., 2007, Temporal and spatial variations of evapotranspiration for spring wheat in the Shiyang river basin in northwest China, Water Resour. Manag., 87(3): 241-250. doi: 10.1016/j.agwat.2006.07.013
DOI
|
21 |
Yun, D. K., Chung, S. O. and Kim, S. J., 2011, Climate change impacts on paddy water requirement, J. Korean Soc. Agric. Eng., 53(4): 39-47. doi:10.5389/KSAE.2011.53.4.037 (in Korean).
DOI
|
22 |
McVicar, T. T., Van Niel, T. G., Li, L., Hutchinson, M. F., Mu, X. and Liu, Z., 2007, Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences, J. Hydrol., 338(3-4): 196-220. doi:10.1016/j.jhydrol.2007.02.018
DOI
|
23 |
Kuo, S. F., Ho, S. S. and Liu, C. W., 2006, Estimation irrigation water requirements with derived crop coefficients for upland and paddy crops in ChiaNan Irrigation Association, Taiwan, Water Resour. Manag., 82(3): 433-451. doi:10.1016/j.agwat.2005.08.002
DOI
|
24 |
Yoo, S. H., Choi, J. Y. and Jang, M. W., 2006, Estimation of paddy rice crop coefficients for FAO Penman-Monteith and modified Penman method. J. Korean Soc. Agric. Eng., 48(1): 13-23. doi:10.5389/KSAE.2006.48.1.013 (in Korean).
DOI
|
25 |
Yoo, S. H., Choi, J. Y., Lee, S. H., Oh, Y. G. and Park, N. Y., 2012, The impacts of climate change on paddy water demand and unit duty of water using high-resolution climate scenarios, J. Korean Soc. Agric. Eng., 54(2): 15-26. doi:10.5389/KSAE.2012.54.2.015 (in Korean).
DOI
|
26 |
Surendran, U., Sushanth, C. M., Mammen, G. and Joseph, E. J., 2015, Modelling the crop water requirement using FAO-CROPWAT and assessment of water resources for sustainable water resource management: A case study in Palakkad district of humid tropical Kerala, India, Aquatic Procedia, 4: 1211-1219. doi:10.1016/j.aqpro.2015.02.154
DOI
|
27 |
Boonwichai, S., Shrestha, S., Babel, M. S., Weesakul, S. and Dattab, A., 2018, Climate change impacts on irrigation water requirement, crop water productivity and rice yield in the Songkhram River Basin, Thailand, J. Clean. Prod., 198: 1157-1164. doi:10.1016/j.jclepro.2018.07.146
DOI
|
28 |
Jie, F., Fei, L. Li, S. Hao, K. Liu, L. and Peng, Y., 2022, Effects on net irrigation water requirement of joint distribution of precipitation and reference evapotranspiration, Agriculture, 12, 801. doi:10.3390/agriculture12060801
DOI
|
29 |
Li, Y., Wang, H, Chen, Y., Deng, M., Li, Q., Wufu, A., Wang, D. and Ma, L., 2020, Estimation of regional irrigation water requirements and water balance in Xinjiang, China during 1995-2017, PeerJ, 8, doi:10.7717/peerj.8243
DOI
|
30 |
Aydin, Y., 2022, Quantification of water requirement of some major crops under semi-arid climate in Turkey, PeerJ, 10. doi:10.7717/peerj.13696
DOI
|
31 |
Kim, D. H., Kim, T. S., Jung, H. C., Jeong, E. S., Lee, S. O. and Jung, C. S., 2020, A benchmarking of electricity industry for improving the integrated water resources management (IWRM) policy, J. Korea Water Resour. Assoc., 53(S-1): 785-795. doi:10.3741/JKWRA.2020.53.S-1.785 (in Korean).
DOI
|
32 |
Park, C. K., Hwang, J. S. and Seo, Y. W., 2020, Improvement of agricultural water demand estimation focusing on paddy water demand, J. Korea Water Resour. Assoc., 53(11): 939-949. doi:10.3741/JKWRA.2020.53.11.939 (in Korean).
DOI
|
33 |
Cho, W. J., Chae, G. S. and Choi, J. Y., 2020, Reforming agricultural water policy for integrated water resources management, P264, KREI (Korea rural economic institute): Naju, Jeonnam, Korea. ISBN:979-11-6149-471-5 93520.
|
34 |
Park, T. S., 2022. The Difference between water management theory and reality for agricultural water, Rural resource, 64(2): 2-14 (in Korean).
|
35 |
Kim, B. J. and Eun, J. H., 2020, Incorporating machine learning into public administration: the role of evidencebased decision-making, Korean Public Administration Review, 54(1): 261-285. doi:10.18333/ KPAR.54.1.261 (in Korean).
DOI
|
36 |
Park, D. S., 2021, Toward digitalization of smart maintenance for water infrastructures. KSCE magazine, 69(3): 20-36 (in Korean).
|
37 |
Breiman, L., 2001, Random forests, Machine Learn, 45(1): 5-32. doi:10.1023/A:1010933404324.
DOI
|
38 |
Geron, A., 2019. Hands-on machine learning with Scikitlearn, Keras, and TensorFlow, 2nd Edition, O'Reilly Media, Inc., ISBN: 9781492032649
|
39 |
Vapnik, V. N., 1999, An overview of statistical learning theory, IEEE Trans. Neural Networks, 10(5): 988-999. doi:10.1109/72.788640.
DOI
|
40 |
Ahmad Yasmin, N. S., Abdul Wahab, N., Ismail, F. S., Musa, M. J., Halim, M. H. A. and Anuar, A. N., 2021, Support vector regression modelling of an aerobic granular sludge in sequential batch reactor, Membranes, 11, 554. doi:10.3390/membranes11080554
DOI
|
41 |
Strobl, C., Malley, J. and Tutz, G., 2009. An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests, Psychol. Methods, 14(4): 323-348. doi:10.1037/a0016973
DOI
|
42 |
Adnan, M. S. G., Rahman,, M. S., Ahmed, N., Ahmed, B., Rabbi, M. F. and Rahman, R. M., 2020, Improving spatial agreement in machine learning-based landslide susceptibility mapping, Remote Sens., 12(20), 3347. doi:10.3390/rs12203347
DOI
|
43 |
Gonzalez, P. F., Bielza, C. and Larranaga, P., 2019, Random forests for regression as a weighted sum of k-potential nearest neighbors, IEEE Access, 7: 25660-25672. doi:10.1109/ACCESS.2019.2900755
DOI
|
44 |
Aldrich, C., 2020, Process variable importance analysis by use of random forests in a shapley regression framework, Minerals, 10(5), 420. doi:10.3390/min10050420
DOI
|
45 |
Yokoyama, A. and Yamaguchio, N., 2020, Optimal hyperparameters for random forest to predict leakage current alarm on premises, E3S Web Conf. 152, 03003. doi:10.1051/e3sconf/202015203003
DOI
|