American Journal of Water Resources

The river Salandi, originated from well-known bio-sphere of Similipal forest and joints with the river Baitarani near Akhandalmani, the Tinitar Ghat after passing through Hadagada dam, Agarpada and Bhadrak town. During the course of journey, it receives forest run off, untreated and semi treated mining wastes, agricultural wastes, industrial wastes of Ferro Alloys Corporation (FACOR) and urban wastes. In the present study, physico-chemical & bacteriological parameters of water were analyzed by collecting water samples from nine different stations in summer (April and May), rainy (August), post rainy (October) and in winter (December) during the year 2015 by applying standard procedures. The mean values of twelve important parameters were calculated for the entire year and were computed to water quality index (WQI) of river Salandi by using the weighted arithmetic index method. The water quality index (WQI) reveals that the quality of water is different at different monitoring stations i.e. quality is good at Hadagada dam, Satabhauni and Dhusuri, poor at Akhandalmani & very poor at Bidyadharapur, very poor and unfit for drinking purpose at Agarpada, Randia (FACOR) and Baudpur and belongs to class ‘C’ river water at Rajghat.

American Journal of Water Resources. 2016, 4(2), 44-53. DOI: 10.12691/ajwr-4-2-3
Pub. Date: June 07, 2016

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Prediction of Missing Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River Basin, Kenya

by Raphael M. Wambua, Benedict M. Mutua and James M. Raude

Original Research

Accurate prediction of missing hydro-meteorological data is crucial in planning, design, development and management of water resources systems. In the present research, prediction of such data using Artificial Neural Networks (ANN) based on temporal and spatial auto-correlation has been conducted for upper Tana River basin in Kenya. Different ANN models were formulated using a combination of numerous data delays in the ANN input layer. The findings show that the best models comprise of a feed-forward neural network trained on Levenberg-Marquardt algorithm with single hidden layer. Additionally, the best ANN architecture model for predicting missing stream flow data was at gauge station 4CC03 with correlation coefficient and MSE of 0732 and 0.242 respectively during validation. Temporal auto-correlation of the observed and the predicted stream flow values were evaluated using a correlation coefficient R that resulted to highest value of 0.756 at gauge station 4AB05. The best ANN model for prediction of missing precipitation data was at station 9037112 with R value of 0.970. In both cases the best performance was at epochs 9 and 20 respectively. The spatial auto-correlation show that the best ANN architecture model for prediction of missing stream flow data was at gauge station 4CC03 with R value of 0.723, while the one for precipitation was at station 9037096 with R value of 0.712 during the validation. The results indicate that the spatial auto-correlation of hydro-meteorological data using ANN is better than the temporal auto-correlation in the data prediction in upper Tana River basin.

American Journal of Water Resources. 2016, 4(2), 35-43. DOI: 10.12691/ajwr-4-2-2
Pub. Date: May 25, 2016

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Assessment of Microbial Quality of Some Selected Shallow Wells in Ogbomoso, South Western Nigeria

by O.S. Olaniyan, D.A. Akeredolu, O.S. Showale and A.S. Akolade

Original Research

The importance of good quality water to the environment has long been realized, hence, the development policy of integrated community. In this research work, the analysis of physical and bacteriological quality of well water was carried out. Thirty (30) samples of the well water were collected and their pH and temperature were determined on the field to avoid interference by changes of environment. The bacteriological analyses were carried out within 24 hours of collection to avoid killing of bacteria present in the water sample. On the average, result shows that six (6) of the wells indicate multiple bacteria, three (3) of the wells indicate single bacteria while no occurrence of bacteria in one of the wells. Shallow well water in ogbomoso is highly contaminated with Salmonella spp, Shigella spp, E.coli spp and it requires disinfection. The entire water sample from the well tested positive to the presence of bacteria which are harmful to human health.

American Journal of Water Resources. 2016, 4(2), 30-34. DOI: 10.12691/ajwr-4-2-1
Pub. Date: May 11, 2016

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