Socio‐Hydrology of Channel Flows in Complex River Basins: Rivers, Canals, and Distributaries in Punjab, Pakistan

Wescoat, James L.; Siddiqi, Afreen; Muhammad, Abubakr

doi:10.1002/2017wr021486

Cited by 45 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e IMF component reflects the nature and real information of the original time series. e instantaneous frequency of time series has a certain physical meaning after it is decomposed by the EMD method [29]. e EMD algorithm is suitable for complex time series of nonstationary and nonlinear data sequences.…”

Section: Complexitymentioning

confidence: 99%

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

et al. 2020

Complexity

View full text Add to dashboard Cite

In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used to predict high-frequency signal sequence, while the ARMA model was used to predict low-frequency signal sequence. Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. The PSO-SVM model’s prediction accuracy decreases in medium- and long-term predictions while the EMD-GM-ARMA model still can maintain high prediction accuracy. Moreover, the relative error fluctuations of the EMD-GM-ARMA model are relatively stable in both short-term and medium-term predictions. This shows that the EMD-GM-ARMA model can provide high-precision predictions with high stability, proving the model to be feasible and effective in predicting the mining safety production situation.

show abstract

Section: Complexitymentioning

confidence: 99%

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

et al. 2020

Complexity

View full text Add to dashboard Cite

show abstract

“…Wescoat et al () use historical geographic and statistical methods to gain context‐specific understanding of how and why measurements of water flows have been done in the ways they have in the Indus River Basin of Pakistan. They do their analysis in three levels of water channels: basin, canal, and local distributary levels.…”

Section: Contributions Of the Papers In The Special Section On Sociohmentioning

confidence: 99%

Expanding the Scope and Foundation of Sociohydrology as the Science of Coupled Human‐Water Systems

Konar

Garcia

Sanderson

et al. 2019

Water Resources Research

View full text Add to dashboard Cite

Sociohydrology was launched as the science dealing with feedbacks between coupled human and water systems. Much of the early work in sociohydrology involved studies in spatially isolated domains (e.g., river basins) dealing with phenomena that involved emergent patterns in the time domain, with a focus on formulating and testing hypotheses about how they arise. The papers collected in this Special Section "Sociohydrology: Spatial and Temporal Dynamics of Coupled Human-Water Systems" illustrate that the scientific scope of sociohydrology has broadened over the last few years, with a rich diversity of phenomena studied and an expansion of the knowledge foundations and methodologies applied. These Special Section papers now incorporate methodologies and approaches from a wide range of social science disciplines, including anthropology, complex systems, economics, and sociology. The major themes tackled by these papers are understanding (i) water metabolism-the economic use of water; (ii) interactions between humans and droughts; (iii) interactions between humans and floods; and (iv) the role of human institutions, policy, and management. These collected papers provide a foundation for future research that strives to understand how to achieve water resources sustainability (society to water) and reduce the risk of hydrological hazards in society (water to society). Going forward, we suggest that the development of a common sociohydrology framework will be paramount for research development and student training. Additionally, increased engagement with the broader water management communities will enhance sociohydrology understanding and impact.

show abstract

“…To thoroughly investigate the proposed models, four rivers' inflow data is used in this study which is comprised of daily rivers inflow (1 st -January to 19 th -June) for the period of 2015-2018. We consider the main river inflow of Indus at Tarbela with its two principal, one left and one right, bank tributaries [38]: Jhelum inflow at Mangla, Chenab at Marala, and Kabul at Nowshera, respectively (see Figure 4). Data is measured in 1000 cusecs.…”

Section: Case Study and Experimental Designmentioning

confidence: 99%

Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Socio‐Hydrology of Channel Flows in Complex River Basins: Rivers, Canals, and Distributaries in Punjab, Pakistan

Cited by 45 publications

References 28 publications

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

Expanding the Scope and Foundation of Sociohydrology as the Science of Coupled Human‐Water Systems

Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis

Contact Info

Product

Resources

About