The Yongding River is the mother river of Beijing. However, due to the environmental pollution caused by the economic development, the water and coastal environment of this river has suffered from great destruction. The ecological restoration of the Yongding River is imperative. In this paper, we analysed seven basic water quality indicators in Yongding River based on Information Entropy and found that the main factors for affecting water quality were ammonia and CODMn. Then the basic water quality indicators were predicted, based on Grey System GM(1,1) model and we concluded that turbidity and conductivity would grow fastest in the next 20 years. Finally, we made some reasonable ideas and methods in Yongding River ecological restoration.

To ensure the safety of drinking water, understanding the trends of water quality in water resource and to provide a scientific basis for water quality management, a three-layer BP neural network is selected to simulate and predict six water quality indicators of the outbound of Dahuofang Reservoir. The six water quality indicators are dissolved oxygen, five days’ biochemical oxygen demand, permanganate index, ammonia nitrogen, total nitrogen and total phosphorus. Training the model with water quality data from 2005 to 2011, Levenberg-Marguardt optimization algorithm is adopted to train samples. After reaching the error requirement, simulate the model with the water quality monitoring data in 2012 and test the model accuracy. Simulation results show that the accuracy of the model prediction is higher in 2012. It is proved that this model can be used to predict water quality of the outbound mouth in Fushun section, and the model provides a theoretical basis for improving the water quality of the reservoir area and can be used to guide the actual water quality management.

The study aims to understand the distribution of groundwater quality in coastal regions from Injambakkam-Thiruvanmyiur areas, Chennai. Groundwater samples were collected from the coastal regions of Tamil Nadu. The objectives of the study are to determine the groundwater quality characteristics such as pH, alkalinity, electrical conductivity, chloride, hardness, total dissolved solids, dissolved oxygen and map the spatial distribution of groundwater quality in the study area by using open source software Quantum GIS (QGIS). The physico-chemical parameters and the quality of the water vary with space and thus mapping it with the GIS is an efficient way to draw conclusions about the study area. Inverse Distance Weighted (IDW) interpolation method was used to create various raster maps which show the spatial distribution. With the present study it is found that, most of the regions in the southern part of the study area have poor to very poor quality based on the Water Quality Index (WQI).

It is very important to accurately predict the river water quality. Prediction of river water quality has been closely watched in water resources evaluation, and is the primary work of scientific planning and management of water resources and exploitation. The accuracy of prediction will directly influence whether we can work out a reasonable plan and management measures. According to the relationship of river water quality with the influencing factors, regression method is used to predict the tendency of river water quality. The influencing factors include rainfall, sediment and runoff. This study could provide reference and guidance for further exploitation of river water.

Principal component analysis is a way to reduce original dimension, to make multiple variables into a few comprehensive index. According to the characteristics of water quality evaluation model, principal component analysis method is developed to evaluate surface water quality using SPSS software at representative sections. By the combination of variables index, adjusting the combinatorial coefficient to make the new variables representative independent. The process is introduced in the paper in detail. The results indicate that the principal component model is suitable for water quality evaluation. By analysis, it is important to pay attention to bring into effective measures for pollution control.

This paper selected Dahuofang Reservoir as the research area, analysed the relationship between water quality and diversion water input, determined the right weight of each index affected the water quality based on correlation analysis and entropy weight method. The results showed that after the diversion water input, the DO is 6739.17t, CODMn is 1735.00t, BOD is 625.83t, NH3-N is12.07t, TP is 12.5t, TN is 1860.75t, coliform-group is 1.38 ×1015. The correlation between the indexes of DO, CODMn, BOD, TN, coliform-group and water quality is significant after diversion water input. The affected right weights of the amount of TN, coliform-group and NH3-N input are more than 0.1, the highest right weight is of TN input (0.1804), followed by coliform-group (0.1173) and NH3-N (0.1165) | the most slight one is TP (0.0164). In terms of comprehensive analysis, the influence of each index of diversion water input on water quality of Dahuofang reservoir, the order is TN>coliform-group>NH3-N>BOD>DO>CODMn>TP.

Analysis of water quality of five tank water samples in Tiptur taluk through 18 physico-chemical parameters, namely WT, pH, DO, BOD, EC, TDS, TA, TH, CO32-, HCO3-, Cl-, SO42-, PO43-, NO3-, Na+, K+, Ca2+ and Mg2+ was taken up during December 2010 to November 2012. A systematic calculation of correlation coefficient among these 18 physico-chemical parameters was carried out using Microsoft excel spreadsheet. A correlation coefficient of 1.00 was found between the TA-HCO3- pair in Eachanur and V. Mallenahalli samples and the pair is perfectly correlated, whereas, ‘r’ value was 0.99 in Halkurke and Honnavalli samples and 0.97 in Albur samples. EC and TDS were perfectly correlated in Halkurke samples and for the same pair ‘r’ value was 0.99 in Eachanur, V. Mallenahalli and Honnavalli samples, whereas it was 0.85 in Albur samples. BOD and PO43- were perfectly correlated in Eachanur samples. When r > ± 0.5 was considered, a total of 22 positive and 3 negative correlations in Eachanur samples | 18 positive and 9 negative correlations in V. Mallenahalli samples | 31 positive and 2 negative correlations in Halkurke samples | 36 positive and 6 negative correlations in Honnavalli samples | and 18 positive and 5 negative correlations in Albur samples were found during the analysis. For a better interpretation of the results, the coefficient of determination was used in addition to ‘r’ value.Analysis of water quality of five tank water samples in Tiptur taluk through 18 physico-chemical parameters, namely WT, pH, DO, BOD, EC, TDS, TA, TH, CO32-, HCO3-, Cl-, SO42-, PO43-, NO3-, Na+, K+, Ca2+ and Mg2+ was taken up during December 2010 to November 2012. A systematic calculation of correlation coefficient among these 18 physico-chemical parameters was carried out using Microsoft excel spreadsheet. A correlation coefficient of 1.00 was found between the TA-HCO3- pair in Eachanur and V. Mallenahalli samples and the pair is perfectly correlated, whereas, ‘r’ value was 0.99 in Halkurke and Honnavalli samples and 0.97 in Albur samples. EC and TDS were perfectly correlated in Halkurke samples and for the same pair ‘r’ value was 0.99 in Eachanur, V. Mallenahalli and Honnavalli samples, whereas it was 0.85 in Albur samples. BOD and PO43- were perfectly correlated in Eachanur samples. When r > ± 0.5 was considered, a total of 22 positive and 3 negative correlations in Eachanur samples | 18 positive and 9 negative correlations in V. Mallenahalli samples | 31 positive and 2 negative correlations in Halkurke samples | 36 positive and 6 negative correlations in Honnavalli samples | and 18 positive and 5 negative correlations in Albur samples were found during the analysis. For a better interpretation of the results, the coefficient of determination was used in addition to ‘r’ value.