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

Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis

Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed component prediction and summation. The proposed architecture is applied on daily rivers inflow time series data of Indus Basin System. The performances of the proposed models are compared with traditional single-stage model (without denoised and decomposed), the hybrid two-stage model (with denoised), and existing three-stage hybrid model (with denoised and decomposition). Three evaluation measures are used to assess the prediction accuracy of all models such as Mean Relative Error (MRE), Mean Absolute Error (MAE), and Mean Square Error (MSE). The proposed, three-stage hybrid models have shown improvement in prediction accuracy with minimum MRE, MAE, and MSE for all case studies as compared to other existing one-stage and two-stage models. In summary, the accuracy of prediction is improved by reducing the complexity of hydrological time series data by incorporating the denoising and decomposition

Biochemical profiling of selected plant extracts and their antifungal activity in comparison with fungicides against Colletotrichum capsici L. causing anthracnose of Chilli

Chili (Capsicum annuum L.) is the utmost significant cash crop of Pakistan. Annually, about 50% chili yield is reduced by chili anthracnose disease caused by Colletotrichum capsici. The current study was conducted to explore the antifungal potential of plant extracts in comparison with commercial fungicides against C. capsici. Morphologically recognized strains of C. capsici were subjected to pathogenicity assay where strain CC-2 showed a highly virulent response. Results from in-vitro studies showed that Ginger (15 % concentration) inhibited fungal mycelial growth and spore germination and results were comparable to Nativo and Antracol at 1000 ppm. From the protective and curative trials, among plant extracts, Ginger at 15% showed maximum crop protective activity (84%) and maximum curative activity (70%). Consequently, among fungicides, Antracol at 1000 ppm showed highest crop protective activity (92%) and maximum curative efficacy (96%). The results of pot experiments showed that among the plant extracts, Ginger significantly inhibited C. capsici and increased plant growth while among fungicides, Antracol was found to be more effective than Nativo. PCA explored the correlation between growth parameters of chili plants treated with plant extracts and fungicides. Biochemical profiling and phytochemical characterization indicated the presence of tannins, phenols, terpenoids, flavonoids, alkaloids, reducing sugars and anthraquinones in ginger and chicory extracts. Ginger showed the highest DPPH scavenging activity (64.9 ± 1.85) as compared to chicory (54.6 ± 2.8). GC–MS analysis of plant extracts revealed the presence of various bioactive compounds including Ethanol, Acetone, 2-Butanone, Trichloromethane, 2-Butanone, 4-(4‑hydroxy-3-methoxyphenyl)-, Gingerol, 1, 2-Benzenedicarboxylic acid, diisooctyl ester, Hexane, Glycerin, Sucrose, Hexadecanoic acid, methyl ester, 9-Octadecenoic acid (Z)-, methyl ester, 1,2-Benzenedicarboxylic acid, mono (2-ethylhexyl) ester, n-Hexadecanoic acid, cis-Vaccenic acid, 1-Monolinoleoylglycerol trimethylsilyl ether, and 9,12,15-Octadecatrienoic acid, 2-[(trimethylsilyl)oxy]-1-[[(trimethylsilyl)oxy] methyl]ethyl ester, (Z,Z,Z). FTIR analysis showed 12, 8 and 13 peak values respectively indicating the presence of important functional groups. NMR analysis showed 4 and 7 peak values of ginger and chicory extracts indicating the structures of functional groups. There is a need to test the disease suppressive potential of plant extracts under field conditions to manage other fatal plant pathogens

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