Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: multiple linear

Sortuj według:

Ogranicz wyniki do:

An identification source of variation on the water quality pattern in the Malacca River basin using chemometric approach

Hua A. K.

Archives of Environmental Protection

2018

Vol. 44, no. 4

111--122

The Malacca River basin experienced river water pollution which caused a major deterioration to the ecosystems and environmental health. This study is carried out to assess the water quality data and identify the pattern of water pollution sources in the study area, and also to develop a predictive performance of water quality in the Malacca River basin. A chemometric approach using a combination of HCA, DA, PCA, and MLR, was applied into twenty water quality variables from nine sampling stations that were collected from January until December of 2015 in the river basin. HCA pointed out three clusters, namely Cluster 1 (C1) with low pollution source, Cluster 2 (C2) with moderate pollution source, and Cluster 3 (C3) with high pollution source. In the DA analysis, the results showed 21 variables, 12 variables, and 9 variables for standard mode, forward stepwise mode, and backward stepwise mode, respectively. Meanwhile, the PCA indicated that the main source of pollutants is detected from residential, industrial, commercial, agricultural, animal livestock, as well as forest land. Among the three models developed from MLR analysis, C3 with a high pollution source is detected to be the most suitable model to be used for the prediction of Water Quality Index in the Malacca River basin. This study proposed for an effective river water quality management by having new water quality monitoring network to be designed for more practical use in order to reduce time and effort, as well as cost saving purposes.

Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers

Ghasemlou K., Aydi M. M., Yildirim M. S.

Archives of Transport

2015

Vol. 34, iss. 2

29--35

Accident prevention is relatively a complex issue considering the effectiveness of the injury prevention technologies as well as more detailed assessment of the complex interactions between the road condition, vehicle and human factor. For many years, highway agencies and vehicle manufacturers showed great efforts to reduce the injuries resulting from the vehicle crashes. Many researchers used a broad range of methods to evaluate the impact of several factors on traffic accidents and injuries. Recent developments lead up to capable for determining the effects of these factors. According to World Health Organization (WHO), cyclists and pedestrians comprise respectively 1.6% and 16.3% in traffic crash fatalities in 2013. Also in Turkey crash fatalities for pedestrian and cyclists are respectively 20.6% and 3% according to Turkish Statistical Instıtute data in 2013. The relationship between cycling and pedestrian rates and injury rates over time is also unknown. This paper aims to predict the crash severity with the traffic injury data of the Konya City in Turkey by implementing the Artificial Neural Networks (ANN), Regression Trees (RT) and Multiple Linear Regression modelling (MLRM) method.