Abstract
Drought affects the natural environment of an area when it persists for a longer period, prompting dry season. Thus, such dry season can have many annihilating effects on river networks. The paper address this predominant issue in the form of an alternate solution which re-routes the course of the natural water sources, like rivers, through those areas, where the water supply is minimal in comparison with the demand, in a cost-effective and highly beneficial manner. In the proposed model, Deep Belief Network (DBN) is utilized to foresee the early event of drought in Marathwada region of Maharashtra. Standard Precipitation Index is used to categorize the severity of drought. Using DBN model, the accuracy obtained with root mean square error of 0.04469, mean absolute error of 0.00207 is far better over the traditional methods. The application of Swarm optimization technique is used to address the problem of drought mitigation through providing a re-routed path.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hao, Z., Hao, F., Singh, V.P., Ouyang, W., Cheng, H.: An integrated package for drought monitoring, prediction and analysis to aid drought modeling and assessment. https://www.sciencedirect.com/science/article/pii/S1364815216302468
Agana, N.A., Homaifar, A.: A Deep learning based approach for long-term drought prediction. https://www.researchgate.net/publication/316943200_A_Deep_Learning_Based_Approach_for_Long-Term_Drought_Prediction
Deo, R.C., Salcedo-Sanz, S., Carro-Calvo, L., Saavedra-Moreno, B.: Drought prediction with standardized precipitation and evapotranspiration index and support vector regression models. In: Integrating Disaster Science and Management. Elsevier Inc. (2018). https://www.researchgate.net/publication/325192054_Drought_Prediction_With_Standardized_Precipitation_and_Evapotranspiration_Index_and_Support_Vector_Regression_Models
Saravanan, M., Sridhar, A., Nikhil Bharadwaj, K., Mohanavalli, S., Srividhya, V.: River network optimization using machine learning. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9142, pp. 409–420. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20469-7_44
Deo, R.C., Tiwari, M.K., Adamowski, J.F., Quilty, J.M.: Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2016). https://www.researchgate.net/publication/303500175_Forecasting_effective_drought_index_using_a_wavelet_extreme_learning_machine_W-ELM_model
Hong, D., Hong, K.A.: Drought forecasting using MLP neural networks. In: 2015 8th International Conference on u- and e-Service, Science and Technology (2015). https://www.researchgate.net/publication/303500175_Forecasting_effective_drought_index_using_a_wavelet_extreme_learning_machine_W-ELM_model
Belayneh, A., Adamowski, J.: Drought forecasting using new machine learning methods. J. Water Land Dev. 18 (1-v1), 3–12. https://www.mcgill.ca/bioeng/files/bioeng/drought_forecasting_using_new_machine_learning_methods.pdf
Area and Production Statistics (APS): Ministry of Agriculture and Farmers Welfare. https://aps.dac.gov.in/LUS/Public/Reports.aspx
Openweather Ltd.: 4 Queens Road, Wimbledon, London, SW198YB, United Kingdom. https://openweathermap.org/
McKee, T.B., Doesken, N.J., Kleist, J.: The relationship of drought frequency and duration to time scale. In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California, 17–22 January 1993, pp. 179–184. American Meteorological Society, Boston (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Subedi, S., Pasalkar, K., Navani, G., Kadam, S., Raghavan Nair Lalitha, P. (2019). Drought Prediction and River Network Optimization in Maharashtra Region. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_37
Download citation
DOI: https://doi.org/10.1007/978-981-13-9942-8_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9941-1
Online ISBN: 978-981-13-9942-8
eBook Packages: Computer ScienceComputer Science (R0)