Aggarwal, C. C. (2015). Data Mining: The Textbook. In Springer. Springer International Publishing Switzerland. https://doi.org/10.1007/978-3-319-14142-8
Bang, S. L., Yang, J. D., & Yang, H. J. (2006). Hierarchical document categorization with k-NN and concept-based thesauri. Information Processing and Management, 42(2), 387–406. https://doi.org/10.1016/j.ipm.2005.04.003
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1993). Classification and Regression Trees. In Chapman & Hall.
Cover, T. M., & Hart, P. E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964
Dumenu, W. K., & Obeng, E. A. (2016). Climate change and rural communities in Ghana: Social vulnerability, impacts, adaptations and policy implications. Environmental Science and Policy, 55, 208–217. https://doi.org/10.1016/j.envsci.2015.10.010
Everard, M. (2020). Managing socio-ecological systems: who, what and how much? The case of the Banas river, Rajasthan, India. Current Opinion in Environmental Sustainability, 44(July 2019), 16–25. https://doi.org/10.1016/j.cosust.2020.03.004
Fahad, S., & Wang, J. (2018). Farmers' risk perception, vulnerability, and adaptation to climate change in rural Pakistan. Land Use Policy, 79(August), 301–309. https://doi.org/10.1016/j.landusepol.2018.08.018
Gain, A. K., Hossain, S., Benson, D., Di Baldassarre, G., Giupponi, C., & Huq, N. (2020). Social-ecological system approaches for water resources management. International Journal of Sustainable Development and World Ecology, 28(2), 109–124. https://doi.org/10.1080/13504509.2020.1780647
Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques. In Intelligent Systems Reference Library (Vol. 12). Springer. https://doi.org/10.1007/978-3-642-19721-5
Grothmann, T., Petzold, M., Ndaki, P., Kakembo, V., Siebenhüner, B., Kleyer, M., Yanda, P., & Ndou, N. (2017). Vulnerability assessment in African villages under conditions of land use and climate change: Case studies from Mkomazi and Keiskamma. Sustainability (Switzerland), 9(6), 1–30. https://doi.org/10.3390/su9060976
Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2004). KNN model-based approach in classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2888(August), 986–996. https://doi.org/10.1007/978-3-540-39964-3_62
Jalal, M. J. E., Khan, M. A., Hossain, M. E., Yedla, S., & Alam, G. M. M. (2021). Does climate change stimulate household vulnerability and income diversity? Evidence from southern coastal region of Bangladesh. Heliyon, 7(9), e07990. https://doi.org/10.1016/j.heliyon.2021.e07990
Kabir, M. J., Cramb, R., Alauddin, M., & Gaydon, D. S. (2019). Farmers' perceptions and management of risk in rice-based farming systems of south-west coastal Bangladesh. Land Use Policy, 86(December 2018), 177–188. https://doi.org/10.1016/j.landusepol.2019.04.040
Lai, Z., Di Chang, Li, S., & Dan Li. (2022). Optimizing land use systems of an agricultural watershed in China to meet ecological and economic requirements for future sustainability. Global Ecology and Conservation, 33(December 2021), e01975. https://doi.org/10.1016/j.gecco.2021.e01975
Montenegro, L., & Hack, J. (2020). A socio-ecological system analysis of multilevel water governance in Nicaragua. Water (Switzerland), 12(6). https://doi.org/10.3390/W12061676
Naik, D. L., & Kiran, R. (2018). Naïve Bayes classifier, multivariate linear regression and experimental testing for classification and characterization of wheat straw based on mechanical properties. Industrial Crops and Products, 112(January), 434–448. https://doi.org/10.1016/j.indcrop.2017.12.034
Octavian, A., Widjayanto, J., Putra, I. N., Purwantoro, S. A., Salleh, M. Z., Rahman, A. A. A., Ismail, A., & Baker, R. (2021). Combined multi-criteria decision making and system dynamics simulation of social vulnerability in southeast asia. Decision Science Letters, 10(3), 323–336. https://doi.org/10.5267/j.dsl.2021.2.005
Opitz, D., & Maclin, R. (1999). Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research, 11(December 1999), 169–198. https://doi.org/10.1613/jair.614
Ostrom, E. (2009). A General Framework for Analyzing Sustainability of Social-Ecological Systems. Science, 362(Juli), 419–422. https://doi.org/10.1126/science.1172133
Quinlan, J. R. (1992). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Inc.
Ramoni, M., & Sebastiani, P. (2001). Robust Bayes classifiers. Artificial Intelligence, 125(1–2), 209–226. https://doi.org/10.1016/S0004-3702(00)00085-0
Riaman, Sukono, Supian, S., & Ismail, N. (2021). Analysing the decision making for agricultural risk assessment: An application of extreme value theory. Decision Science Letters, 10(3), 351–360. https://doi.org/10.5267/j.dsl.2021.2.003
Singh, G., & Pandey, A. (2021). Flash flood vulnerability assessment and zonation through an integrated approach in the Upper Ganga Basin of the Northwest Himalayan region in Uttarakhand. International Journal of Disaster Risk Reduction, 66(September), 102573. https://doi.org/10.1016/j.ijdrr.2021.102573
Tran, P. T., Vu, B. T., Ngo, S. T., Tran, V. D., & Ho, T. D. N. (2022). Climate change and livelihood vulnerability of the rice farmers in the North Central Region of Vietnam: A case study in Nghe An province, Vietnam. Environmental Challenges, 7(May 2021), 100460. https://doi.org/10.1016/j.envc.2022.100460
Wichern, J., Descheemaeker, K., Giller, K. E., Ebanyat, P., Taulya, G., & van Wijk, M. T. (2019). Vulnerability and adaptation options to climate change for rural livelihoods – A country-wide analysis for Uganda. Agricultural Systems, 176(June), 102663. https://doi.org/10.1016/j.agsy.2019.102663
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data Mining: Practical Machine Learning Tools and Techniques. In Morgan Kaufmann. Fourth Edition.
Yang, W., Xu, K., Lian, J., Ma, C., & Bin, L. (2018). Integrated flood vulnerability assessment approach based on TOPSIS and Shannon entropy methods. Ecological Indicators, 89(December 2017), 269–280. https://doi.org/10.1016/j.ecolind.2018.02.015
Ye, Y., Wei, X., Fang, X., & Li, Y. (2017). Social vulnerability assessment by mapping population density and pressure on cropland in Shandong Province in China during the 17th-20th century. Sustainability (Switzerland), 9(7), 1–14. https://doi.org/10.3390/su9071171
Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for kNN Classification. ACM Transactions on Intelligent Systems and Technology, 8(3). https://doi.org/10.1145/2990508
Zuniga-Teran, A. A., Mussetta, P. C., Lutz Ley, A. N., Díaz-Caravantes, R. E., & Gerlak, A. K. (2021). Analyzing water policy impacts on vulnerability: Cases across the rural-urban continuum in the arid Americas. Environmental Development, 38(November 2019), 100552. https://doi.org/10.1016/j.envdev.2020.100552