Fuzzy Regression Model to Assess the Rainfall Variability Trend inKalahandi, Odisha: Leveraging 100 Years of Data for Trend Robustnessand Predictive Accuracy
DOI:
https://doi.org/10.31181/sa41202668Keywords:
Fuzzy regression model, Rainfall variability, Trend analysis, Predictive analysis, KBK regionAbstract
Linear regression analysis is one of the most common statistical methods used to investigate trends in rainfall over time. However, conventional regression models usually require datasets that have been
completely and accurately determined for trend analysis with rainfall, the datasets often include monthly or annual averages which can result in uncertainty in trend analysis. In order to incorporate this uncertainty,
fuzzy set theory represents the ambiguity from the analyzed datasets. The fuzzy regression models consider both input and output variables to be Triangular fuzzy numbers. This study proposes a fuzzy regression approach that describes the relationships between precipitation and time in a context of excessive variability in rainfall which creates challenges for the sustainable management of water resources.
Kalahandi district in Odisha has been acknowledged as one of the impoverished districts in the KBK Region. The utilization of the fuzzy regression model in addressing trend variability or trends in rainfall
in Kalahandi, Odisha is a comprehensive investigation of historical rainfall records related to trends and variability of rainfall in Kalahandi, especially the region’s severe effects associated with climate change; it is of utmost importance to understand how rainfall variability trends have changed over time for local agrariens or agriculture workers and policy-makers to recognize changes in rainfall patterns and develop adapted agriculture plans and manage water resources in a variable climate. The data collection process included 100 years of fuzzy rainfall data (1923-2023) which helps to frame current rainfall variability trends in Kalahandi. From this study, it has been shown that Kalahandi is experiencing climate change or other environmental factors that are leading to a substantial increase in rainfall variability. The study methodologically establishes fuzzy regression as an alternate approach to provide a more accurate predictive model for uncertain rainfall.
References
A. Panda, N. Sahu, S. Behera, T. Sayama, L. Sahu, R. Avtar, R. Singh, and M. Yamada, “Impact of climate variability on crop
yield in kalahandi, bolangir, and koraput districts of odisha, india,” Climate, vol. 7, no. 11, p. 126, 2019.
A. Panda and N. Sahu, “Trend analysis of seasonal rainfall and temperature pattern in kalahandi, bolangir and koraput districts
of odisha, india,” Atmospheric Science Letters, vol. 20, no. 10, p. e932, 2019.
L. A. Zadeh, “Fuzzy sets,” Information and control, vol. 8, no. 3, pp. 338–353, 1965.
M. Smithson and J. Verkuilen, Fuzzy set theory: Applications in the social sciences. No. 147, Sage, 2006.
K. K. Mohanta, Design and Application of Data Envelopment Analysis under the Extended Fuzzy Environment. PhD thesis,
Indira Gandhi National Tribal University, 2024.
L. Sahoo, T. Senapati, and R. R. Yager, Real life applications of multiple criteria decision making techniques in fuzzy domain,
vol. 420. Springer Nature, 2022.
V. Chaubey, D. S. Sharanappa, K. K. Mohanta, and R. Verma, “A malmquist fuzzy data envelopment analysis model for
performance evaluation of rural healthcare systems,” Healthcare Analytics, vol. 6, p. 100357, 2024.
K. K. Mohanta, D. S. Sharanappa, V. Chaubey, and D. Dabke, “A novel ranking approach for solving the fuzzy lpp and fuzzy
dea model,” European Chemical Bulletin, vol. 12, no. 4, pp. 9300–9311, 2023.
M. Russo, Fuzzy learning and applications. CRC press, 2024.
A. M. Al-Lami, Y. K. Al-Timimi, and A. M. Al-Salihi, “Innovative trend analysis of annual rainfall in iraq during 1980-2021,”
Journal of Agrometeorology, vol. 26, no. 2, pp. 196–203, 2024.
J. Mohanta and A. C. Pathy, “Investigating rainfall patterns in south singhbhum: A non-parametric approach utilizing
mann-kendall tests and sen’s slope estimator,” Asian Journal of Geographical Research, vol. 7, no. 4, pp. 45–66, 2024.
A. C. Pathy and J. Mohanta, “Soil moisture, reservoir, and groundwater interactions: Managing water sustainability in south
singbhum,” Asian Journal of Geological Research, vol. 8, no. 2, pp. 342–361, 2025.
S. M. Dhak, “Rainfall trend analysis in tehsils of palghar district, maharashtra state, india,” International Journal for Research
in Applied Science & Engineering Technology, vol. 9, no. 11, pp. 238–253, 2021.
A. Saini and N. Sahu, “Decoding trend of indian summer monsoon rainfall using multimethod approach: (century long indian
monsoon rainfall trend),” Stochastic Environmental Research and Risk Assessment, vol. 35, no. 11, pp. 2313–2333, 2021.
N. Alahacoon, K. Matheswaran, P. Pani, and G. Amarnath, “A decadal historical satellite data and rainfall trend analysis
(2001–2016) for flood hazard mapping in sri lanka,” Remote Sensing, vol. 10, no. 3, p. 448, 2018.
U. Rathnayake, “Comparison of statistical methods to graphical methods in rainfall trend analysis: case studies from tropical
catchments,” Advances in Meteorology, vol. 2019, no. 1, p. 8603586, 2019.
K. Sharma, A. Singh, and S. Dubey, “Rainfall trend analysis for crop planning under rainfed conditions in district agra of uttar
pradesh,” Mausam, vol. 69, no. 4, pp. 599–606, 2018.
A. Saini, N. Sahu, P. Kumar, S. Nayak, W. Duan, R. Avtar, and S. Behera, “Advanced rainfall trend analysis of 117 years over
west coast plain and hill agro-climatic region of india,” Atmosphere, vol. 11, no. 11, p. 1225, 2020.
S. Adarsh and M. J. Reddy, “Trend analysis of rainfall in four meteorological subdivisions of southern india using nonparametric
methods and discrete wavelet transforms.,” International journal of Climatology, vol. 35, no. 5, 2015.
M. J. Alam and A. Majumder, “The statistical analysis of rainfall trend and its variability (1901–2020) in kolkata, india,”
Bulletin of Geography. Physical Geography Series, no. 23, pp. 5–16, 2022.
S. L. Bora, K. Bhuyan, P. J. Hazarika, J. Gogoi, and K. Goswami, “Analysis of rainfall trend using non-parametric methods and
innovative trend analysis during 1901–2020 in seven states of north east india,” Current Science, vol. 122, no. 7, pp. 801–811,
R. Khan, M. Sharif, and M. Shakeel, “Trend analysis of rainfall and temperature of periyar river basin,” in IOP Conference
Series: Earth and Environmental Science, vol. 1326, p. 012143, IOP Publishing, 2024.
S. Şenocak and M. F. Emek, “Evaluation of monthly precipitation amount in eastern anatolia region by using trend analysis
methods,” Avrupa Bilim ve Teknoloji Dergisi, no. 17, pp. 807–822, 2019.
C. K. Ng, J. L. Ng, Y. F. Huang, Y. X. Tan, and M. Mirzaei, “Tropical rainfall trend and stationarity analysis,” Water Supply,
vol. 20, no. 7, pp. 2471–2483, 2020.
A. Kruger and M. Nxumalo, “Historical rainfall trends in south africa: 1921–2015,” Water Sa, vol. 43, no. 2, pp. 285–297, 2017.
A. Khalil, “Combined use of graphical and statistical approaches for rainfall trend analysis in the mae klong river basin,
thailand,” Journal of Water and Climate Change, vol. 14, no. 12, pp. 4642–4668, 2023.
E. C. Koskei, J. J. Kitetu, and C. W. Recha, “Analysis of spatial variability in rainfall trends in baringo county, kenya,” African
Journal of Environmental Science and Technology, vol. 12, no. 9, pp. 296–304, 2018.
M. M. Monir, M. Rokonuzzaman, S. C. Sarker, E. Alam, M. K. Islam, and A. R. M. T. Islam, “Spatiotemporal analysis and
predicting rainfall trends in a tropical monsoon-dominated country using makesens and machine learning techniques,” Scientific
Reports, vol. 13, no. 1, p. 13933, 2023.
T. Ogunbode, V. Esan, T. Samson, O. Oyelowo, and J. Asifat, “Rainfall trend and its implications for sustainable crop
production and water resources management: a case study of iwo, nigeria,” Journal of Applied Sciences and Environmental
Management, vol. 26, no. 8, pp. 1415–1422, 2022.
M. Farhangi, M. Kholghi, and S. A. Chavoshian, “Rainfall trend analysis of hydrological subbasins in western iran,” Journal of
Irrigation and Drainage Engineering, vol. 142, no. 10, p. 05016004, 2016.
M. M. Alemu and G. T. Bawoke, “Analysis of spatial variability and temporal trends of rainfall in amhara region, ethiopia,”
Journal of Water and Climate Change, vol. 11, no. 4, pp. 1505–1520, 2020.
M. G. Hiben, A. G. Awoke, and A. A. Ashenafi, “Hydrological modeling and evaluation of water balance over the complex topography
of nile basin headwaters: The case of ghba river, northern ethiopia,” International Research Journal of Multidisciplinary
Technovation, vol. 5, no. 6, pp. 19–42, 2023.
V. N. Barai and R. M. Kalunge, “Rainfall trend analysis.,” vol. 17, p. 121–125, 2021.
R. Morbidelli, C. Saltalippi, J. Dari, and A. Flammini, “Effect of time-resolution of rainfall data on trend estimation for annual
maximum depths with a duration of 24 hours,” Water, vol. 13, no. 22, p. 3264, 2021.
S. Lertworasirikul, S.-C. Fang, J. A. Joines, and H. L. Nuttle, “Fuzzy data envelopment analysis (dea): a possibility approach,”
Fuzzy sets and Systems, vol. 139, no. 2, pp. 379–394, 2003.
H. Tanaka, I. Hayashi, and J. Watada, “Possibilistic linear regression analysis for fuzzy data,” European Journal of Operational
Research, vol. 40, no. 3, pp. 389–396, 1989.
P. Diamond and H. Tanaka, “Fuzzy regression analysis,” in Fuzzy sets in decision analysis, operations research and statistics,
pp. 349–387, Springer, 1998.
H. Ishibuchi and H. Tanaka, “Fuzzy regression analysis using neural networks,” Fuzzy sets and systems, vol. 50, no. 3,
pp. 257–265, 1992.
M. Sakawa and H. Yano, “Multiobjective fuzzy linear regression analysis for fuzzy input-output data,” Fuzzy sets and Systems,
vol. 47, no. 2, pp. 173–181, 1992.
P.-Y. Hao and J.-H. Chiang, “Fuzzy regression analysis by support vector learning approach,” IEEE Transactions on Fuzzy
Systems, vol. 16, no. 2, pp. 428–441, 2008.
J.-S. Jang, “Anfis: adaptive-network-based fuzzy inference system,” IEEE transactions on systems, man, and cybernetics,
vol. 23, no. 3, pp. 665–685, 1993.
S. Donoso, N. Marín, and M. A. Vila, “Fuzzy regression with quadratic programming: An application to financial data,” in
International Conference on Intelligent Data Engineering and Automated Learning, pp. 1304–1311, Springer, 2006.
M. Khashei, A. Z. Hamadani, and M. Bijari, “A novel hybrid classification model of artificial neural networks and multiple
linear regression models,” Expert Systems with Applications, vol. 39, no. 3, pp. 2606–2620, 2012.
Downloads
Published
Issue
Section
How to Cite
All site content, except where otherwise noted, is licensed under the 