Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods

Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle swarm optimization (PSO), the genetic algorithm (GA), the grey wolf optimization (GWO), the social spider optimization (SSO), the salp swarm algorithm (SSA) and the hunger games search algorithm (HGS) were used to forecast droughts based on the standard precipitation index (SPI). Monthly precipitation data from three stations in Bangladesh were used in the applications. The accuracy of the methods was compared by forecasting four SPI indices, SPI3, SPI6, SPI9, and SPI12, using the root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). The HGS algorithm provided a better performance than the alternative algorithms, and it considerably improved the accuracy of the RVFL method in drought forecasting; the improvement in RMSE for the SPI3, SP6, SPI9, and SPI12 was by 6.14%, 11.89%, 14.14%, 24.5% in station 1, by 6.02%, 17.42%, 13.49%, 24.86% in station 2 and by 7.55%, 26.45%, 15.27%, 13.21% in station 3, respectively. The outcomes of the study recommend the use of a HGS-based RVFL in drought modeling.

Hybrid point and interval prediction approaches for drought modeling using ground-based and remote sensing data

Drought as a severe natural disaster has devastating effects on the environment; therefore, reliable drought prediction is an important issue. In the current study, based on lower upper bound estimation, hybrid models including data preprocessing, permutation entropy, and artificial intelligence (AI) methods were used for point and interval predictions of short- to long-term series of Standardized Precipitation Index in the Northwest of Iran. Ground-based and remote sensing precipitation data were used covering the period of 1983–2017. In the modeling process, first, the data processing capability via variational mode decomposition (VMD), ensemble empirical mode decomposition, and permutation entropy (PE) was investigated in drought point prediction. Then, interval prediction was applied for tolerating increased uncertainty and providing more details for practical operation decisions. The simulation results demonstrated that the proposed integrated models could achieve significantly better performance compared to single models. Hybrid PE models increased the modeling accuracy up to 40 and 55%. Finally, the efficiency of developed models was verified for Normalized Difference Vegetation Index prediction. Results demonstrated that the proposed methodology based on remote sensing data and VMD–PE–AI approaches could be successfully used for drought modeling, especially in limited or non-gauged areas.

Study of water regime parameters in apples during drought

Relevance. Recently, the number of hot and dry years has increased significantly. Under the influence of drought, the leaves of the apple tree prematurely fall, the ovaries and fruits fall off, which significantly affects on the yield. In this regard, the study of the water regime of apple remains relevant under drought conditions.Methods. The studies were carried out on the basis of the laboratory of physiology of fruit plant resistance at VNIISPK in 2016-2017. Apple cultivars of the VNIISPK breeding growing on the semi-dwarf rootstock 54-118 were studied. Antonovka Obyknovennaya was taken as a standard cultivar. The experiments were laid in 2013, the spacing scheme was 5 m x 3 m. The crown shape is of spindle type. The row-spacing and near-trunk stripes are kept under the fall fallow. The method of artificial dehydration was used to determine drought resistance of apple cultivars. The apple cultivars were studied with the aim to determine the physiological parameters of water regime relative to their drought resistance.Results. As a result of the two-year studies, the cultivars were characterized by the average content of water in leaves (61.2-65.1%). Water deficiency in most varieties was optimal in the field and did not exceed 10.0%. The increase in water deficiency in apple leaves was noted in drought modeling. For two years, of the leaf tissues water deficiency was in leaves of the Veniaminovskoye cultivar both in the field (5.2%) and after drought modeling (22.4%). During the growing season, the distribution of precipitation and temperature during the passage of individual phenophases by apple plants influenced the overall water content in the leaf tissues. The decrease of the water content in the leaf tissues and the increase of water deficiency were observed when dry conditions occurred. The decrease of the water regime and water deficiency in leaves was notes to a greater extent during the formation of fruits. It was found that all of the studied apple cultivars had an average level of resistance to drought. The study of water regime parameters showed that Veniaminovskoye was characterized by more stable indices and this indicated greater resistance to drought.