Optimal cropping pattern based on short-term streamflow forecasts to improve agricultural economic benefits and crop productivity under uncertainty conditions

References

• Bahrami, S., Ardejani, F.D., and Baafi, E., 2016. Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine. Journal of Hydrology, 536, 471–484. doi:10.1016/j.jhydrol.2016.03.002.
   Web of Science ®Google Scholar
• Bai, Y., et al., 2016. Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. Journal of Hydrology, 532, 193–206. doi:10.1016/j.jhydrol.2015.11.011.
   Web of Science ®Google Scholar
• Broad, K., et al., 2007. Climate, stream flow prediction and water management in northeast Brazil: societal trends and forecast value. Climatic Change, 84 (2), 217–239. doi:10.1007/s10584-007-9257-0.
   Web of Science ®Google Scholar
• Fahimi, F., Yaseen, Z.M., and El-shafie, A., 2017. Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theoretical and Applied Climatology, 128 (3), 875–903. doi:10.1007/s00704-016-1735-8.
   Web of Science ®Google Scholar
• Fathian, F., et al., 2019. Hybrid models to improve the monthly river flow prediction: integrating artificial intelligence and non-linear time series models. Journal of Hydrology, 575, 1200–1213. doi:10.1016/j.jhydrol.2019.06.025.
   Web of Science ®Google Scholar
• Government of Assam (GoA), Assam Human Development Report 2014 Guwahati Available from: http://niti.gov.in/writereaddata/files/human-development/Assam_HDR_30Sep2016.pdf
   Google Scholar
• Govindaraju Rao, S., 2000. Artificial neural networks in hydrology. Journal of Hydrologic Engineering, 5 (April), 124–137. doi:10.1007/978-94-015-9341-0.
   Google Scholar
• Huang, G., Baetz, B.W., and Patry, G.G., 1992. A grey linear programming approach for municipal solid waste management planning under uncertainty. Civil Engineering Systems, 9 (4), 319–335. doi:10.1080/02630259208970657.
   Web of Science ®Google Scholar
• Koutsoyiannis, D., Yao, H., and Georgakakos, A., 2008. Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods. Hydrological Sciences Journal, 53 (1), 142–164. doi:10.1623/hysj.53.1.142.
   Web of Science ®Google Scholar
• Kurukulasuriya, P. and Mendelsohn, R., 2008. Crop switching as a strategy for adapting to climate change. African Journal of Agricultural and Resource Economics, 2 (1), 105–126. http://ideas.repec.org/a/ags/afjare/56970.html
   Google Scholar
• Li, X., et al., 2017. Applying uncertain programming model to improve regional farming economic benefits and water productivity. Agricultural Water Management, 179, 352–365. doi:10.1016/j.agwat.2016.06.030.
   Web of Science ®Google Scholar
• Maas, A.L., Hannun, A.Y., and Ng, A.Y., 2013. Rectifier nonlinearities improve neural network acoustic models. In: ICML Workshop on Deep Learning for Audio, Speech and Language Processing, 28.
   Google Scholar
• Mahto, A.K., et al., 2021. Short-term forecasting of agriculture commodities in context of indian market for sustainable agriculture by using the artificial neural network. Journal of Food Quality, 2021, 1–13. doi:10.1155/2022/1860460.
   Web of Science ®Google Scholar
• Mutlu, E., et al., 2008. Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrological Processes, 22 (26), 5097–5106. doi:10.1002/hyp.7136.
   Web of Science ®Google Scholar
• Regulwar, D.G. and Gurav, J.B., 2012. Sustainable irrigation planning with imprecise parameters under fuzzy environment. Water Resources Management, 26 (13), 3871–3892. doi:10.1007/s11269-012-0109-y.
   Web of Science ®Google Scholar
• Sharma, A. and Goyal, M.K., 2018. Assessment of ecosystem resilience to hydroclimatic disturbances in India. Global Change Biology, 24 (2), e432–e441. doi:10.1111/gcb.13874.
   PubMed Web of Science ®Google Scholar
• Stephen, G., 1988. nonlinear neural networks: principles, mechanisms, and architectures. Nonlinear Neural Networks: Principles, Mechanisms, and Architectures, 1 (1), 17–61. doi:10.1016/0893-6080(88)90021-4.
   Web of Science ®Google Scholar
• Talukdar, G., Sarma, A.K., and Bhattacharjya, R.K., 2020. Mapping agricultural activities and their temporal variations in the riverine ecosystem of the Brahmaputra River using geospatial techniques. Remote Sensing Applications: Society and Environment, Elsevier, 20 (August), 100423. doi:10.1016/j.rsase.2020.100423.
   Google Scholar
• Talukdar, G., Sarma, A.K., and Bhattacharjya, R.K., 2021. Sediment analysis and modelling based approach for optimal allocation of riverine sandbar for socio economic benefits. Ecological Engineering, 173 (September), 106415. doi:10.1016/j.ecoleng.2021.106415.
   Web of Science ®Google Scholar
• Talukdar, G., Sarma, A.K., and Bhattacharjya, R.K., 2022. Integrating optimization and damage estimation to increase economic benefit and ensure food security under seasonal land variability. Journal of Environmental Management, 320, 115872. doi:10.1016/j.jenvman.2022.115872.
   PubMed Web of Science ®Google Scholar
• Tucci, C.E., et al., 2008. Short and long term flow forecasting in the Rio Grande watershed (Brazil). Atmospheric Science Letters, 9 (2), 53–56. doi:10.1002/asl.165.
   Web of Science ®Google Scholar
• Van den Dool, H., 2007. Empirical methods in short-term climate prediction. Oxford: Oxford University Press. doi:10.1093/oso/9780199202782.001.0001.
   Google Scholar
• Wang, R., Li, Y., and Tan, Q., 2015. A review of inexact optimization modeling and its application to integrated water resources management. Frontiers of Earth Science, 9 (1), 51–64. doi:10.1007/s11707-014-0449-4.
   Web of Science ®Google Scholar
• Wilby, R.L., Wedgbrow, C.S., and Fox, H.R., 2004. Seasonal predictability of the summer hydrometeorology of the River Thames, UK. Journal of Hydrology, 295 (1–4), 1–16. doi:10.1016/j.jhydrol.2004.02.015.
   Web of Science ®Google Scholar
• Wu, S. and Huang, G.H., 2007. An interval-parameter fuzzy approach for multiobjective linear programming under uncertainty. Journal of Mathematical Modelling and Algorithms, 6 (2), 195–212. doi:10.1007/s10852-006-9042-5.
   Google Scholar
• Xu, B., et al., 2015. Empirical evaluation of rectified activations in convolutional network. Available from: http://arxiv.org/abs/1505.00853
   Google Scholar
• Yao, H. and Georgakakos, A., 2001. Assessment of folsom lake response to historical and potential future climate scenarios 2. Reservoir Management. Journal of Hydrology, 249 (1–4), 176–196. doi:10.1016/S0022-1694(01)00418-8.
   Web of Science ®Google Scholar
• Yaseen, Z.M., et al., 2015. Artificial intelligence based models for stream-flow forecasting: 2000–2015. Journal of Hydrology, 530, 829–844. doi:10.1016/j.jhydrol.2015.10.038
   Web of Science ®Google Scholar
• Zhang, L.D., et al., 2014. Deficit irrigation optimization model for crop area allocation under uncertainties in Shiyang River basin, China. Sensor Letters, 12 (3–4), 867–875. doi:10.1166/sl.2014.3120.
   Google Scholar
• Zhang, J., et al., 2018. Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918–929. doi:10.1016/j.jhydrol.2018.04.065.
   Web of Science ®Google Scholar
• Zimmermann, H.J., 1978. Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1 (1), 45–55. doi:10.1016/0165-0114(78)90031-3.
   Google Scholar