Morpho-palynological investigation of gymnospermal flora from subalpine and alpine zones of northern Pakistan using LM and SEM.

The pollen morphology, with special reference to exine sculpture, of some species of the gymnosperms was assessed for the first time from the subalpine and alpine zones of western Himalayas northern Pakistan. The pollen of all these species is airborne and allergenic, so pollen morphology helps for identification of this allergenic pollen at specific level. Different morpho-palynological characteristics were analyzed including size range of pollen, polar and equatorial diameter ratio, exine ornamentation, sculpturing, exine thickness, pollen type, and shape. For accurate and quick identification of species, taxonomic key was made based on different morpho-palynological characteristics. The quantitative data were processed using SPSS software. Gymnospermal pollen includes inaperturate, rarely 1-colpate observed in (Cupressaceae), hexazonocolpate in (Ephedraceae), vesiculate, bissacate in (Pinaceae), and inaperturate in (Taxaceae). Different pollen shapes observed were prolate (4 spp), sub-spheroidal (7 spp), and oblate (1 spp). Variation was observed in exine sculpturing granular (4 spp), reticulate (1 spp), areolate-punctate (3 spp), and psilate (2 spp). This is based on the analysis of 10 plants belonging to four families of gymnosperms. Distinct pollen shape has emerged as the most diagnostic feature to separate some genera such as spheroidal in (Cupressaceae, Taxaceae), prolate and radiosymmetrical in (Ephedraceae), and bilateral in (Pinaceae). Exine thickness and sculpturing proved to be helpful at generic and specific levels. The results reinforced the significance of gymnospermal pollen morphological features which were used as aid for valuable taxonomic tool in plant systematics.

Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model.

Several data-driven and hybrid models are univariate and not considered the dependance structure of multivariate random variables, especially the multi-site river inflow data, which requires the joint distribution of the same river basin system. In this paper, we proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Vine copula-based approach to address this issue. The proposed hybrid model comprised on two stages: In the first stage, the CEEMDAN is used to extract the high dimensional multi-scale features. Further, the multiple models are used to predict multi-scale components and residuals. In the second stage, the residuals obtained from the first stage are used to model the joint uncertainty of multi-site river inflow data by using Canonical Vine. For the application of the proposed two-step architecture, daily river inflow data of the Indus River Basin is used. The proposed two-stage methodology is compared with only the first stage proposed model, Vector Autoregressive and copula-based Autoregressive Integrated Moving Average models. The four evaluation measures, that is, Mean Absolute Relative Error (MARE), Mean Absolute Deviation (MAD), Nash-Sutcliffe Efficiency (NSE) and Mean Square Error (MSE), are used to observe the prediction performance. The results demonstrated that the proposed model outperforms significantly with minimum MARE, MAD, NSE, and MSE for two case studies having significant joint dependance. Therefore, it is concluded that the prediction can be improved by appropriately modeling the dependance structure of the multi-site river inflow data.

Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model.

River inflow prediction plays an important role in water resources management and power-generating systems. But the noises and multi-scale nature of river inflow data adds an extra layer of complexity towards accurate predictive model. To overcome this issue, we proposed a hybrid model, Variational Mode Decomposition (VMD), based on a singular spectrum analysis (SSA) denoising technique. First, SSA his applied to denoise the river inflow data. Second, VMD, a signal processing technique, is employed to decompose the denoised river inflow data into multiple intrinsic mode functions (IMFs), each with a relative frequency scale. Third, Empirical Bayes Threshold (EBT) is applied on non-linear IMF to smooth out. Fourth, predicted models of denoised and decomposed IMFs are established by learning the feature values of the Support Vector Machine (SVM). Finally, the ensemble predicted results are formulated by adding the predicted IMFs. The proposed model is demonstrated using daily river inflow data from four river stations of the Indus River Basin (IRB) system, which is the largest water system in Pakistan. To fully illustrate the superiority of our proposed approach, the SSA-VMD-EBT-SVM hybrid model was compared with SSA-VMD-SVM, VMD-SVM, Empirical Mode Decomposition (EMD) based i.e., EMD-SVM, SSA-EMD-SVM, Ensemble EMD (EEMD) based i.e., EEMD-SVM and SSA-EEMD-SVM. We found that our proposed hybrid SSA-EBT-VMD-SVM model outperformed than others based on following performance measures: the Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Therefore, SSA-VMD-EBT-SVM model can be used for water resources management and power-generating systems using non-linear time series data.

An improved framework to predict river flow time series data.

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF's and noise free IMF's are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF's are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.