Skip to main content

Advertisement

SpringerLink
Log in

Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India

  • Research
  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

The intensity and frequency of diverse hydro-meteorological disasters viz., extreme droughts, severe floods, and cyclones have increasing trends due to unsustainable management of land and water resources, coupled with increasing industrialization, urbanization and climate change. This study focuses on the forecasting of drought using selected Artificial Neural Network (ANN)-based models to enable decision-makers to improve regional water management plans and disaster mitigation/reduction plans. Four ANN models were developed in this study, viz., one conventional ANN model and three hybrid ANN models: (a) Wavelet based-ANN (WANN), (b) Bootstrap based-ANN (BANN), and (c) Wavelet-Bootstrap based-ANN (WBANN). The Standardized Precipitation Evapotranspiration Index (SPEI), the best drought index identified for the study area, was used as a variable for drought forecasting. Three drought indices, such as SPEI-3, SPEI-6 and SPEI-12 respectively representing “short-term”, “intermediate-term”, and “long-term” drought conditions, were forecasted for 1-month to 3-month lead times for six weather stations over the study area. Both statistical and graphical indicators were considered to assess the performance of the developed models. For the hybrid wavelet model, the performance was evaluated for different vanishing moments of Daubechies wavelets and decomposition levels. The best-performing bootstrap-based model was further used for analysing the uncertainty associated with different drought forecasts. Among the models developed for drought forecasting for 1 to 3 months, the performances of the WANN and WBANN models are superior to the simple ANN and BANN models for the SPEI-3, SPEI-6, and SPEI-12 up to the 3-month lead time. The performance of the WANN and WBANN models is the best for SPEI-12 (MAE = 0.091-0.347, NSE = 0.873-0.982) followed by SPEI-6 (MAE = 0.258-0.593; NSE = 0.487-0.848) and SPEI-3 (MAE = 0.332-0.787, NSE = 0.196-0.825) for all the stations up to 3-month lead time. This finding is supported by the WBANN analyze uncertainties as narrower band width for SPEI-12 (0.240-0.898) as compared to SPEI-6 (0.402-1.62) and SPEI-3 (0.474-2.304). Therefore, the WBANN model is recommended for the early warning of drought events as it facilitates the uncertainty analysis of drought forecasting results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The data presented in this study are available on request from the corresponding author.

Abbreviations

ANN:

Artificial Neural Network

ARMA:

Autoregressive Moving Average

BANN:

Bootstrap based-ANN

CI:

Confidence Interval

CWT:

Continuous Wavelet Transformation

DL:

Decomposition Level

DWT:

Discrete Wavelet Transformation

EDI:

Effective Drought Index

ENSO:

El Niño-Southern Oscillation

ET:

Evapotranspiration

MAE:

Mean Absolute Error

MW:

Mother Wavelet

NSE:

Nash-Sutcliffe Efficiency

NADI:

Nonlinear Aggregated Drought Index

PDN:

Precipitation Departure from Normal

RMSE:

Root Mean Square Error

SPEI:

Standardized Precipitation Evapotranspiration Index

SPEI-3:

Standardized Precipitation Evapotranspiration Index with a 3-month scale

SPEI-6:

Standardized Precipitation Evapotranspiration Index with a 6-month scale

SPEI-12:

Standardized Precipitation Evapotranspiration Index with a 12-month scale

SPI:

Standardized Precipitation Index

WANN:

Wavelet based-ANN

WBANN:

Wavelet-Bootstrap –based ANN

WT:

Wavelet Transformation

References

  • Adamowski, J. F. (2008). Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. Journal of Hydrology, 353(3), 247–266.

    Google Scholar 

  • AghaKouchak, A., Pan, B., Mazdiyasni, O., Sadegh, M., Jiwa, S., Zhang, W., et al. (2022). Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting. Philosophical Transactions of the Royal Society A, 380(2238), 20210288.

    Google Scholar 

  • Alawsi, M. A., Zubaidi, S. L., Al-Bdairi, N. S. S., Al-Ansari, N., & Hashim, K. (2022). Drought forecasting: a review and assessment of the hybrid techniques and data pre-processing. Hydrology, 9(7), 115. https://doi.org/10.3390/hydrology9070115

    Article  Google Scholar 

  • Allen, R. G., Perevia, L. S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. In FAO Irrigation and Drainage Paper No. 56, Food and Agriculture Organization (FAO).

    Google Scholar 

  • Anshuka, A., van Ogtrop, F. F., & Willem Vervoort, R. (2019). Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis. Natural Hazards, 97, 955–977.

    Google Scholar 

  • ASCE. (2000a). Application of Artificial Neural Networks in Hydrology. Artificial neural networks in hydrology I: Preliminary concepts. Journal of Hydrologic Engineering, 5(2), 115–123.

    Google Scholar 

  • ASCE. (2000b). Application of Artificial Neural Networks in Hydrology. Artificial neural networks in hydrology II: Hydrologic Applications. Journal of Hydrologic Engineering, 5(2), 124–137.

    Google Scholar 

  • Behrangi, A., Nguyen, H., & Granger, S. (2015). Probabilistic seasonal prediction of meteorological drought using the bootstrap and multivariate information. Journal of Applied Meteorology and Climatology, 54(7), 1510–1522.

    Google Scholar 

  • Belayneh, A., & Adamowski, J. (2012). Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression. Applied Computational Intelligence and Soft Computing, Article, 794061, 13. https://doi.org/10.1155/2012/794061

    Article  Google Scholar 

  • Belayneh, A., Adamowski, J., Khalil, B., & Ozga-Zielinski, B. (2014). Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. Journal of Hydrology, 508, 418–429.

    Google Scholar 

  • Belayneh, A., Adamowski, J., Khalil, B., & Quilty, J. (2016). Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmospheric Research, 172, 37–47.

    Google Scholar 

  • Beven, K. (2006). A manifesto for the equifinality thesis. Journal of Hydrology, 320(1), 18–36.

    Google Scholar 

  • Beyaztas, U., Arikan, B. B., Beyaztas, B. H., & Kahya, E. (2018). Construction of prediction intervals for Palmer Drought Severity Index using bootstrap. Journal of Hydrology, 559, 461–470.

    Google Scholar 

  • Bogner, K., & Pappenberger, F. (2011). Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system. Water Resources Research, 47. https://doi.org/10.1029/2010WR009137

  • Byun, H. R., & Wilhite, D. A. (1999). Objective quantification of drought severity and duration. Journal of Climatology, 12, 2747–2756.

    Google Scholar 

  • Cannas, B., Fanni, A., See, L., & Sias, G. (2006). Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Physics and Chemistry of the Earth, Parts A/B/C, 31(18), 1164–1171.

    Google Scholar 

  • Crespo, J. L., & Mora, E. (1993). Drought estimation with neural networks. Advances in Engineering Software, 18(3), 167–170.

    Google Scholar 

  • Deo, R. C., & Şahin, M. (2015). Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydro-meteorological parameters and climate indices in eastern Australia. Atmospheric Research, 161, 65–81.

    Google Scholar 

  • Dikshit, A., Pradhan, B., & Alamri, A. M. (2020). Temporal hydrological drought index forecasting for New South Wales, Australia using machine learning approaches. Atmosphere, 11(6), 585. https://doi.org/10.3390/atmos11060585

    Article  Google Scholar 

  • Djerbouai, S., & Souag-Gamane, D. (2016). Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the Algerois Basin in North Algeria. Water Resources Management, 30(7), 2445–2464.

    Google Scholar 

  • Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall.

    Google Scholar 

  • Fung, K. F., Huang, Y. F., Koo, C. H., & Soh, Y. W. (2020). Drought forecasting: A review of modelling approaches 2007–2017. Journal of Water and Climate Change, 11(3), 771–799.

    Google Scholar 

  • Hailegeorgis, T. T., Thorolfsson, S. T., & Alfredsen, K. (2013). Regional frequency analysis of extreme precipitation with consideration of uncertainties to update IDF curves for the city of Trondheim. Journal of Hydrology, 498, 305–318.

    Google Scholar 

  • Han, D., Kwong, T., & Li, S. (2007). Uncertainties in real-time flood forecasting with neural networks. Hydrological Processes, 21(2), 223–228.

    Google Scholar 

  • Hargreaves, G. H., & Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1(2), 96–99.

    Google Scholar 

  • Haykin, S. (1994). Neural Networks. Macmillan College Publishing Company, Inc.

    Google Scholar 

  • Hinge, G., Piplodiya, J., Sharma, A., Hamouda, M. A., & Mohamed, M. M. (2022). Evaluation of Hybrid Wavelet Models for Regional Drought Forecasting. Remote Sensing, 14(24), 6381. https://doi.org/10.3390/rs14246381

    Article  Google Scholar 

  • Hong, X., Guo, S., Zhou, Y., & Xiong, L. (2015). Uncertainties in assessing hydrological drought using streamflow drought index for the upper Yangtze River basin. Stochastic Environmental Research and Risk Assessment, 29(4), 1235–1247.

    Google Scholar 

  • Hu, Y. M., Liang, Z. M., Liu, Y. W., Wang, J., Yao, L., & Ning, Y. (2015). Uncertainty analysis of SPI calculation and drought assessment based on the application of Bootstrap. International Journal of Climatology, 35(8), 1847–1857.

    Google Scholar 

  • Huang, Y. F., Mirzaei, M., & Amin, M. Z. M. (2016). Uncertainty quantification in rainfall intensity duration frequency curves based on historical extreme precipitation quantiles. Procedia Engineering, 154, 426–432.

    Google Scholar 

  • Jha, M. K. (Ed.). (2010). Natural and Anthropogenic Disasters: Vulnerability, Preparedness and Mitigation (pp. 1–18). Springer, Berlin, Germany and Capital Publishing Company, New Delhi, India.

    Google Scholar 

  • Jha, M. K., & Sahoo, S. (2015). Efficacy of neural network and genetic algorithm techniques in simulating spatio-temporal fluctuations of groundwater. Hydrological Processes, 29(5), 671–691.

    Google Scholar 

  • Jia, Y., & Culver, T. B. (2006). Bootstrapped artificial neural networks for synthetic flow generation with a small data sample. Journal of Hydrology, 331(3-4), 580–590.

    Google Scholar 

  • Kasiviswanathan, K. S., & Sudheer, K. P. (2017). Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models. Stochastic Environmental Research and Risk Assessment, 31, 1659–1670.

    Google Scholar 

  • Katipoğlu, O. M. (2022). Prediction of future hydrological droughts with tree-based algorithms. International Journal of Engineering Science, 51, 51–70.

    Google Scholar 

  • Katipoğlu, O. M. (2023). Prediction of streamflow drought index for short-term hydrological drought in the semi-arid Yesilirmak Basin using Wavelet transform and artificial intelligence techniques. Sustainability, 15(2), 1109. https://doi.org/10.3390/su15021109

    Article  Google Scholar 

  • Katipoğlu, O. M., & Acar, R. (2022). Space-time variations of hydrological drought severities and trends in the semi-arid Euphrates Basin, Turkey. Stochastic Environmental Research and Risk Assessment, 36, 4017–4040.

    Google Scholar 

  • Keshavarz, M., Karami, E., & Vanclay, F. (2013). The social experience of drought in rural Iran. Journal of Land Use Policy, 30, 120–129.

    Google Scholar 

  • Keskin, M. E., Terzi, O., Taylan, E. D., & Kucukyaman, D. (2011). Meteorological drought analysis using artificial neural networks. Scientific Research and Essays, 6(21), 4469–4477.

    Google Scholar 

  • Khalil, A. F., McKee, M., Kemblowski, M., Asefa, T., & Bastidas, L. (2006). Multiobjective analysis of chaotic dynamic systems with sparse learning machines. Advances in Water Resources, 29(1), 72–88.

    Google Scholar 

  • Khan, M. M. H., Muhammad, N. S., & El-Shafie, A. (2020). Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting. Journal of Hydrology, 590, 125380. https://doi.org/10.1016/j.jhydrol.2020.125380

    Article  Google Scholar 

  • Kim, T. W., & Valdés, J. B. (2003). Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, ASCE, 8(6), 319–328.

    Google Scholar 

  • Liu, Y. W., Wang, W., Hu, Y. M., & Liang, Z. M. (2014). Drought assessment and uncertainty analysis for Dapoling basin. Natural Hazards, 74(3), 1613–1627.

    Google Scholar 

  • Maheswaran, R., & Khosa, R. (2012). Comparative study of different wavelets for hydrologic forecasting. Computers & Geosciences, 46, 284–295.

    Google Scholar 

  • Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications. Environmental Modeling and Software, 15, 101–124.

    Google Scholar 

  • Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.

    Google Scholar 

  • Masud, M. B., Khaliq, M. N., & Wheater, H. S. (2015). Analysis of meteorological droughts for the Saskatchewan River Basin using univariate and bivariate approaches. Journal of Hydrology, 522, 452–466.

    Google Scholar 

  • Mélèse, V., Blanchet, J., & Molinié, G. (2018). Uncertainty estimation of Intensity–Duration–Frequency relationships: A regional analysis. Journal of Hydrology, 558, 579–591.

    Google Scholar 

  • Mirzaei, M., Huang, Y. F., El-Shafie, A., Chimeh, T., Lee, J., Vaizadeh, N., & Adamowski, J. (2015). Uncertainty analysis for extreme flood events in a semi-arid region. Natural Hazards, 78, 1947–1960.

    Google Scholar 

  • Mishra, A. K., & Desai, V. R. (2006). Drought forecasting using feed-forward recursive neural network. Ecological Modeling, 198, 127–138.

    Google Scholar 

  • Mishra, A. K., Desai, V. R., & Singh, V. P. (2007). Drought forecasting using a hybrid stochastic and neural network model. Journal of Hydrologic Engineering, ASCE, 12(6), 626–638.

    Google Scholar 

  • Młyński, D., Wałęga, A., & Kuriqi, A. (2021). Influence of meteorological drought on environmental flows in mountain catchments. Ecological Indicators, 133, 108460.

    Google Scholar 

  • Mohapatra, J. B., Jha, P., Jha, M. K., & Biswal, S. (2021). Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India. Science of the Total Environment, 785, 147319. https://doi.org/10.1016/j.scitotenv.2021.147319

    Article  CAS  Google Scholar 

  • Morid, S., Smakhtin, V., & Bagherzadeh, K. (2007). Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology, 27(15), 2103–2111.

    Google Scholar 

  • Mulualem, G. M., & Liou, Y. A. (2020). Application of artificial neural networks in forecasting a standardized precipitation evapotranspiration index for the Upper Blue Nile basin. Water, 12(3), 643.

    Google Scholar 

  • Ng, J. L., Abd Aziz, S., Huang, Y. F., Mirzaei, M., Wayayok, A., & Rowshon, M. K. (2019). Uncertainty analysis of rainfall depth duration frequency curves using the bootstrap resampling technique. Journal of Earth System Science, 128, 1–15.

    Google Scholar 

  • Nguyen, Q. H., Ly, H. B., Ho, L. S., Al-Ansari, N., Le, H. V., Tran, V. Q., et al. (2021). Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/4832864

  • Nourani, V., Baghanam, A. H., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358–377.

    Google Scholar 

  • Ochoa-Rivera, J. C. (2008). Prospecting droughts with stochastic artificial neural networks. Journal of Hydrology, 352(1), 174–180.

    Google Scholar 

  • Overeem, A., Buishand, A., & Holleman, I. (2008). Rainfall depth-duration-frequency curves and their uncertainties. Journal of Hydrology, 348(1-2), 124–134.

    Google Scholar 

  • PACS. (2004). Drought in India: Challenges and Initiatives. In Report of Poorest Areas Civil Society (PACS) Programme 2001-2008.

    Google Scholar 

  • Partal, T. (2009). Modelling evapotranspiration using discrete wavelet transform and neural networks. Hydrological Processes, 23(25), 3545–3555.

    Google Scholar 

  • Sahoo, S., & Jha, M. K. (2013). Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrogeology Journal, 21(8), 1865–1887.

    Google Scholar 

  • Seo, Y., Kim, S., & Singh, V. P. (2013). Flood forecasting and uncertainty assessment using bootstrapped ANFIS. In Proceedings of 6th Conference of Asia Pacific Association of Hydrology and Water Resources, Seoul, South Korea, pp (pp. 1–8).

    Google Scholar 

  • Shahfahad, Talukdar, S., Ali, R., Nguyen, K. A., Naikoo, M. W., Liou, Y. A., et al. (2022). Monitoring drought pattern for pre-and post-monsoon seasons in a semi-arid region of western part of India. Environmental Monitoring and Assessment, 194(6), 396. https://doi.org/10.1007/s10661-022-10028-5

    Article  CAS  Google Scholar 

  • Shirmohammadi, B., Moradi, H., Moosavi, V., Semiromi, M. T., & Zeinali, A. (2013). Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran). Natural Hazards, 69(1), 389–402.

    Google Scholar 

  • Srivastav, R. K., Sudheer, K. P., & Chaubey, I. (2007). A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resources Research, 43(10). https://doi.org/10.1029/2006WR005352

  • Tufaner, F., & Özbeyaz, A. (2020). Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms. Environmental Monitoring and Assessment, 192, 1–14.

    Google Scholar 

  • Twomey, J. M., & Smith, A. E. (1998). Bias and variance of validation methods for function approximation neural networks under conditions of sparse data. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 417–430.

    Google Scholar 

  • UNESCO Water Portal. (2008). Water and natural disasters in celebration of International Day for Natural Disaster Reduction. UNESCO Water Portal Bi-monthly Newsletter, No. 209, 14 October 2008, http://www.emwis.org/thematicdirs/news/unesco-water-portal-bi-monthly-newsletter-no-209

  • Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696–1718.

    Google Scholar 

  • Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., Angulo, M., & El Kenawy, A. (2010). A new global 0.5 gridded dataset (1901–2006) of a multiscalar drought index: comparison with current drought index datasets based on the Palmer Drought Severity Index. Journal of Hydrometeorology, 11(4), 1033–1043.

    Google Scholar 

  • Wable, P. S., & Jha, M. K. (2018). Application of Archimedean copulas to the impact assessment of hydro-climatic variables in semi-arid aquifers of western India. Hydrogeology Journal, 26(1), 89–108.

    Google Scholar 

  • Wable, P. S., Jha, M. K., & Shekhar, A. (2018). Comparison of drought indices in a semi-arid river basin of India. Water Resources Management, 33(1), 75–102.

    Google Scholar 

  • Wu, Z., Yin, H., He, H., & Li, Y. (2022). Dynamic-LSTM hybrid models to improve seasonal drought predictions over China. Journal of Hydrology, 615. https://doi.org/10.1016/j.jhydrol.2022.128706

Download references

Acknowledgements

The authors would like to acknowledge various government departments/institutions like India Meteorology Department, Pune, Mahatma Phule Agricultural University, Rahuri, and State Data Storage Centre, Nashik, for providing the required hydrometeorological data and information to carry out this study. The authors are also very grateful to the Associate Editor and reviewers for their helpful comments/suggestions that improved the manuscript significantly.

Funding

The first author is thankful to the Ministry of Human Resource Development (currently known as the Ministry of Education), New Delhi, India for the financial support through scholarship.

Author information

Authors and Affiliations

  1. Department of Soil Science, Kerala Forest Research Institute, Peechi, Thrissur, India

    Pawan S. Wable

  2. AgFE Department, Indian Institute of Technology Kharagpur, Kharagpur, India

    Madan Kumar Jha & Sabinaya Biswal

  3. Central Island Agricultural Research Institute, Andaman and Nicobar Islands, Port Blair, India

    Sirisha Adamala

  4. College of Agricultural Engineering and Technology, Anand Agricultural University, Anand, India

    Mukesh Kumar Tiwari

Authors
  1. Pawan S. Wable
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Madan Kumar Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sirisha Adamala
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mukesh Kumar Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sabinaya Biswal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualisation: Pawan S. Wable, Madan Kumar Jha; Methodology: Pawan S. Wable, Madan Kumar Jha; Software: Pawan S. Wable, Sirisha Adamala, Mukesh Kumar Tiwari; Data Collection: Pawan S. Wable; Writing-original draft preparation: Pawan S. Wable; Writing-review and editing: Madan Kumar Jha, Sirisha Adamala, Sabinaya Biswal; Supervision: Madan Kumar Jha. All the authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Madan Kumar Jha.

Ethics declarations

Ethical Approval

This article does not contain any studies on human participants or animals performed by any authors. This study was done according to the ethical standards.

Ethical Responsibilities of Authors

All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors.

Consent to Participate

None.

Competing Interests

None.

Conflict of Interest

None.

Consent to Publish

None.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

ESM 1

(DOCX 816 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wable, P.S., Jha, M.K., Adamala, S. et al. Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India. Environ Monit Assess 195, 1090 (2023). https://doi.org/10.1007/s10661-023-11631-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-023-11631-w

Keywords

Search

Navigation