Articles | Volume 23, issue 4
Research article
20 Apr 2023
Research article |  | 20 Apr 2023

Hydrological drought forecasting under a changing environment in the Luanhe River basin

Min Li, Mingfeng Zhang, Runxiang Cao, Yidi Sun, and Xiyuan Deng

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Cited articles

Abbasi, A., Khalili K., Behmanesh, J., and Shirzad, A.: Estimation of ARIMA model parameters for drought prediction using the genetic algorithm, Arab. J. Geosci., 14, 841–841,, 2021. 
Aghelpour, P. and Varshavian, V.: Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA), Complexity, 2021, 6610228,, 2021. 
Ahnadi, M.: Climatic drought forecasting using artificial neural network in Hamedan region, New York Science Journal, 4, 15–19,, 2011. 
Alquraish, M. A., Abuhasel, K. S., and Alqahtani, A. K.: SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting, Sustainability, 13, 12576–12576,, 2021. 
Behzad, A. and Hamid, M.: Revisiting hydrological drought propagation and recovery considering water quantity and quality, Hydrol. Process., 33, 1492–1505,, 2019.  
Short summary
It is an important disaster reduction strategy to forecast hydrological drought. In order to analyse the impact of human activities on hydrological drought, we constructed the human activity factor based on the method of restoration. With the increase of human index (HI) value, hydrological droughts tend to transition to more severe droughts. The conditional distribution model involving of human activity factor can further improve the forecasting accuracy of drought in the Luanhe River basin.
Final-revised paper