Input variable selection for water resources systems using a modified minimum redundancy maximum relevance (mMRMR) algorithm     

Mohamad I. Hejazi  Ximing Cai 《Advances in water resources》2009

Input variable selection (IVS) is a necessary step in modeling water resources systems. Neglecting this step may lead to unnecessary model complexity and reduced model accuracy. In this paper, we apply the minimum redundancy maximum relevance (MRMR) algorithm to identifying the most relevant set of inputs in modeling a water resources system. We further introduce two modified versions of the MRMR algorithm (α-MRMR and β-MRMR), where α and β are correction factors that are found to increase and decrease as a power-law function, respectively, with the progress of the input selection algorithms and the increase of the number of selected input variables. We apply the proposed algorithms to 22 reservoirs in California to predict daily releases based on a set from a 121 potential input variables. Results indicate that the two proposed algorithms are good measures of model inputs as reflected in enhanced model performance. The α-MRMR and β-MRMR values exhibit strong negative correlation to model performance as depicted in lower root-mean-square-error (RMSE) values.  相似文献   

Groundwater Quality Modeling with a Small Data Set          下载免费PDF全文

Mohamad Sakizadeh  Abbass Malian  Eisa Ahmadpour 《Ground water》2016,54(1):115-120

Seventeen groundwater quality variables collected during an 8‐year period (2006 to 2013) in Andimeshk, Iran, were used to implement an artificial neural network (NN) with the purpose of constructing a water quality index (WQI). The method leading to the WQI avoids instabilities and overparameterization, two problems common when working with relatively small data sets. The groundwater quality variables used to construct the WQI were selected based on principal component analysis (PCA) by which the number of variables were decreased to six. To fulfill the goals of this study, the performance of three methods (1) bootstrap aggregation with early stopping; (2) noise injection; and (3) ensemble averaging with early stopping was compared. The criteria used for performance analysis was based on mean squared error (MSE) and coefficient of determination (R²) of the test data set and the correlation coefficients between WQI targets and NN predictions. This study confirmed the importance of PCA for variable selection and dimensionality reduction to reduce the risk of overfitting. Ensemble averaging with early stopping proved to be the best performed method. Owing to its high coefficient of determination (R² = 0.80) and correlation coefficient (r=0.91), we recommended ensemble averaging with early stopping as an accurate NN modeling procedure for water quality prediction in similar studies.  相似文献   

Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting     

Ramesh Murlidhar  Bhatawdekar  Yazdani Bejarbaneh  Behnam  Jahed Armaghani  Danial  Mohammed  Ahmed Salih  Tonnizam Mohamad  Edy 《Natural Resources Research》2021,30(2):1865-1887

Natural Resources Research - In surface mines and underground excavations, every blasting operation can have some destructive environmental impacts, among which air overpressure (AOp) is of major...  相似文献   

Factors encouraging sustainability integration into institutions of higher education     

K. S. Chiong  Z. F. Mohamad  A. R. Abdul Aziz 《International Journal of Environmental Science and Technology》2017,14(4):911-922

In view of the mountainous evidence on destruction of environmental quality and societal well-being as a consequence of rapid economic development, sustainability has gained vast attention from the community and industrial players. Tertiary education is a platform through which sustainability can be inculcated within the society as it imparts knowledge and provides various trainings. There has been extensive research on factors that encourage sustainability integration into Institutions of Higher Education in the last decade. However, majority of the previous publications only discuss one or two factors exclusively and there is no literature that summarizes and discusses such factors in a collective manner. This paper provides an overview of the main factors that encourage sustainability integration into Institutions of Higher Education in the last decade. It aims at providing a one-stop reference for future researchers who need a reference on factors that encourage sustainability integration into Institutions of Higher Education, especially those who are interested in conducting a progressive research in this context. Accordingly, a review of relevant publications from year 2000 and above was conducted and it was found that there are generally eight main factors, which encourage sustainability integration into Institutions of Higher Education, which are: (1) integration into curricula; (2) suitable pedagogy; (3) campus management; (4) research; (5) opportunities provision; (6) availability of social capital; (7) awareness level; and (8) community outreach. There is no indicator on the impact level of these factors, and thus, it is suggested that relevant research can be conducted in future.  相似文献   

Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network   总被引：1，自引：0，他引：1  

Mohamad Javad Alizadeh  Hosein Shahheydari  Mohammad Reza Kavianpour  Hamid Shamloo  Reza Barati 《Environmental Earth Sciences》2017,76(2):86

The longitudinal dispersion coefficient is a key element in determining the distribution and transmission of pollution, especially when cross-sectional mixing is completed. However, the existing predictive techniques for this purpose exhibit great amounts of uncertainty. The main objective of this study is to present a more accurate model for predicting longitudinal dispersion coefficient in natural rivers and streams. Bayesian network (BN) approach was considered in the modeling procedure. Two forms of input variables including dimensional and dimensionless parameters were examined to find the best model structure. In order to increase the performance of the model, the clustering method as a preprocessing data technique was applied to categorize the data in separate groups with similar characteristics. An expansive data set consisting of 149 field measurements was used for training and testing steps of the developed models. Three performance evaluation criteria were adopted for comparison of the results of the different models. Comparison of the present results with the artificial neural network (ANN) model and also well-known existing equations showed the efficiency of the present model. The performance of dimensionless BN model 30% is more than dimensional ones in terms of the root mean square error. The accuracy criterion was increased from 70 to 83% by performing clustering analysis on the BN model. The BN-cluster model 43% is more accurate than ANN model in terms of the accuracy criterion. The results indicate that the BN-cluster model give 16% better results than the best available considered model in terms of the accuracy criterion. The developed model provides a suitable approach for predicting pollutant transport in natural rivers.  相似文献