A New Agricultural Drought Index to Characterize Agricultural Drought Using Data Mining Techniques
Abstract
Drought monitoring is a critical task as its occurrence and extent vary according to many factors like drought type, risk, agricultural losses, and impact. Monitoring drought is important because the footprint of this hazard is larger than that of other natural hazards. Many drought indices are developed to monitor complex drought conditions. The intensity and severity of drought in a particular region and at a particular time can be tracked by the drought indicator. In this research, a new agricultural drought index, Yield-Evapotranspiration Drought Index (YEDI) is developed using crop yield, potential, and reference crop evapotranspiration. Data mining and Neural Network techniques have been used to model the drought index. The agricultural and climatic data used is selected from the year 1983 to 2015 (33 years) from the period of June to October (Kharif period) for Maharashtra state in India. The drought index generates the positive values which are further divided into a range of high, medium, and low intensities of drought. SPI and SPEI indices are used for validation against YEDI. Results show that there is a correlation between YEDI and SPEI whereas a low correlation is between YEDI and SPI. YEDI proves to be useful for agricultural drought monitoring.
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[2] Salee, W.; Pongsapukdee, V. (2013). Odds Prediction of Drought Category Using Log Linear Models Based on SPI in Northeast of Thailand. Silpakorn U Science and TechJ, 7(1), 32-40.
[3] Kaur, A., Sood, S. (2020) Cloud-Centric IoT-Based Green Framework for Smart Drought Prediction, IEEE IoT Journa. 7(2), 1111-1121.
[4] Zargar, A. (2011). A review of drought indices, Environ. Rev , 19, 333–349.
[5] Mishra, A.K., Singh, V.P. (2011). Drought Modeling-A Review, Journal of Hydrology, 3 (49), 157-175.
[6] Agwata, J. F. (2014). A Review of Some Indices used for Drought Studies, Civil and Environmental Research, 6(2), 14-21.
[7] Sun, L.; Mitcheel, S.; Davidson, A. (2011). Multiple Drought Indices for Agricultural Drought Risk Assessment on Canadian Prairies. Int. Journal of Climatology, Royal Meteorological Society. 32(11), 1628-1639.
[8] Bayissa, Y. (2018). Comparison of the Performance of Six Drought Indices in Characterizing Historical Drought for the Upper Blue Nile Basin, Ethiopia. Geosciences. , 8 (81), 1-26.
[9] Bachmair. (2016). Drought indicators revisited: the need for a wider consideration of environment and society. WIREs Water, 3(4), 516-536.
[10] Svoboda, M.; Fuchs, B.A. (2016). Handbook of Drought Indicators and Indices, World Meteorological Organization (WMO) and Global Water Partnership (GWP). Integrated Drought Management Programme (IDMP), Integrated Drought Management Tools and Guidelines Series, Geneva.
[11] Wang, Y. (2020). Evaluation of the suitability of six drought indices in naturally growing, transitional vegetation zones in Inner Mongolia (China). PLoS ONE, 15(5), 1-15.
[12] Bendjema, L. (2019). Characterization of the climatic drought indices application to the Mellah catchment, North-East of Algeria, Journal of Water and Land Development. 2019(43), 28-40.
[13] Labedzki, L.; Bak, B. (2017). Impact of meteorological drought on crop water deficit and crop yield reduction in Polish agriculture. Journal of Water and Land Development, 2017(34), 181–190.
[14] Chen, S. (2020). Comprehensive Drought Assessment Using a Modified Composite Drought index: A case study in Hubei Province, China. Water, 12(462), 1-12.
[15] Wang, K. (2019). Exploring Drought Conditions in the Three River Headwaters Region from 2002 to 2011 Using Multiple Drought Indices. Water, 11(190), 1-20.
[16] Gallardo, M. (2019). Response of Crop Yield to Different Time scales of Drought in United States: Spatio-Temporal Patterns and Climatic and Environmental Drivers, Agricultural and Forest Meteorology, 264, 40-55.
[17] Mayoor, M. (2018). Comparison of Four Precipitation Based Drought Indices in Marathwada Region of Maharashtra India. International Journal of Advance and Innovative Research, 5(4), 60-70.
[18] Hao, Z.; Aghakouchak, A. (2014). A Nonparametric Multivariate Multi-Index Drought Monitoring Framework. Journal of Hydrometeorology, 15, 89-101.
[19] Shin, J. (2018). Investigation of drought propagation in South Korea using drought index and conditional probability. Terr. Atmos. Ocean. Sci, 29(2), 231-241.
[20] Zhang, X. (2017). Multi-sensor integrated framework and index for agricultural drought monitoring. Remote Sensing of Environment, 188, 141–163.
[21] Esfahanian, E. (2015). Development and evaluation of a comprehensive drought index, Journal of Environmental Management, 185, 31-43.
[22] Alwan, I. (2018). Comparison of Nine Meteorological Drought Indices Over Middle Euphrates Region During Period from 1988 To 2017. International Journal of Engineering & Technology, 7(4), 602-607.
[23] Blauhut, V. (2016). Estimating drought risk across Europe from reported drought impacts, drought indices, and vulnerability factors, Hydrol. Earth Syst. Sci., 20, 2779–2800.
[24] Tian, Y. (2018). Agricultural drought prediction using climate indices based on support vector regression in Xiangjiang river basin, Science of the total environment, 622-623, 710- 720.
[25] Balbo, F. (2019). The evaluation of drought indices: Standard Precipitation Index, Standard Precipitation Evapotranspiration Index, and Palmer Drought Severity Index in Cilacap-Central Java. IOP Conf. Series: Earth and Environmental Science, 303, 1-10.
[26] Kerman C. and Gul G., Comparing Two Streamflow-Based Drought Indices, In Gastescu, Water resources and wetlands, 4th International Conference Water resources and wetlands, Tulcea (Romania), 5-9 September 2018.
[27] Okal, H. (2020). Spatio-temporal characterisation of droughts using selected indices in Upper Tana River watershed, Kenya. Scientific African, 7, 1-12.
[28] Pathak,A.; Dodamani, C. (2016). Comparison of two hydrological drought indices. Perspectives in Science, 8, 626-628.
[29] Jain, V. (2016). Comparison of drought indices for appraisal of drought characteristics in the Ken River Basin, Weather and Climate Extremes, 8, 1–11.
[30] Thomas, T. (2016). Reconnaissance drought index based evaluation of meteorological drought characteristics in Bundelkhand. Procedia Technology, 24, 23- 30.
[31] Wang, K. (2015). Analysis of spatio-temporal evolution of droughts in Luanhe River Basin using different drought indices. Water Science and Engineering, 8.
[32] Report: India State of Forest Report, 2019, 152-162.
[33] Choudhary, K., Tahlani, P., Bisen, P., Saxena, R., Ray, S. (2017) Assessment of drought indicators. Technical report, New Delhi.
[34] NIDM: Maharashtra. In: National Disaster Risk Reduction Portal, Maharashtra, 2012, 1-26.
[35] Liu, X. (2020). A remote sensing and artificial neural network-based integrated agricultural drought index: Index development and applications, Catena, 2020(186), 1-8.
[36] Mamane Barkawi Mansour Badamassi, M. B. M.; El-Aboudi, A.; Gbetkom, P. G. (2020). A New Index to Better Detect and Monitor Agricultural Drought in Niger Using Multisensor Remote Sensing Data. The Professional Geographer, DOI: 10.1080/00330124.2020.1730197, 1730197, 1-12.
[37] Wang, H.; Rogers, J.C.; Munroe, D.K. (2015). Commonly used drought indices as indicators of soil moisture in China. J. Hydrometeorol.
[38] Tian, T. (2018). Evaluation of six indices for monitoring agricultural drought in the southcentral United States, Agricultural and Forest Meteorology, 249(2018), 107-119.
[39] Gayathri, A. (2016). A survey on weather forecasting by data mining. IJARCCE, 5(2), 298–300.
[40] Netti, K.; Radhika, Y. (2016). Minimizing loss of accuracy for seismic hazard prediction using Naive Bayes classifier. IRJET, 3(4), 75–77.
[41] Heaton, J. (2013). Bayesian networks for predictive modeling. Forecast, 6–10.
[42] Ali, Z. (2017). Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model. Advances in Meteorology, 2017, 1-9.
[43] Hamoud, A. (2018). Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis, International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 26-31.
[44] Reddy, A.; Pachouri, S. (2016). Comprehensive Study on Efficient Diabetes Disease Prediction with Using Various Advance Decision Tree Models Algorithms. International Journal of Computer Techniques, 3(5), 57-65.
[45] Amjad, A. S. (2016). Educational Data Mining & Students’ Performance Prediction. International Journal of Advanced Computer Science and Applications, 7(2016), 212-220.
Authors
Wankhede, S., Armstrong, L. ., & Tripathy , A. K. . (2024). A New Agricultural Drought Index to Characterize Agricultural Drought Using Data Mining Techniques . International Journal of Advanced Science and Computer Applications, 4(1). https://doi.org/10.47679/ijasca.v4i1.63
This work is licensed under a Creative Commons Attribution 4.0 International License.