American Journal of Water Resources

ISSN (Print): 2333-4797

ISSN (Online): 2333-4819

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

1Egerton University Department of Agricultural Engineering, Kenya

2Jomo Kenyatta University of Agriculture and Technology, SWEED, Kenya

American Journal of Water Resources. 2016, 4(2), 35-43
doi: 10.12691/ajwr-4-2-2
Copyright © 2016 Science and Education Publishing

Cite this paper:
Raphael M. Wambua, Benedict M. Mutua, James M. Raude. Prediction of Missing Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River Basin, Kenya. American Journal of Water Resources. 2016; 4(2):35-43. doi: 10.12691/ajwr-4-2-2.

Correspondence to: Raphael  M. Wambua, Egerton University Department of Agricultural Engineering, Kenya. Email:


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.



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Assessment of Water Quality Index of River Salandi at Hadagada Dam and Its Down Stream upto Akhandalmani, Bhadrak, Odisha, India

1Department of Chemistry, A.B College, Basudevpur, Bhadrak, Odisha, India

2Department of Chemistry, VSSUT, Burla, Odisha, India

3Department of Chemistry, Bhadrak Autonomous College, Bhadrak, Odisha, India

American Journal of Water Resources. 2016, 4(2), 44-53
doi: 10.12691/ajwr-4-2-3
Copyright © 2016 Science and Education Publishing

Cite this paper:
Pratap Kumar Panda, Rahas Bihari Panda, Prasant Kumar Dash. Assessment of Water Quality Index of River Salandi at Hadagada Dam and Its Down Stream upto Akhandalmani, Bhadrak, Odisha, India. American Journal of Water Resources. 2016; 4(2):44-53. doi: 10.12691/ajwr-4-2-3.

Correspondence to: Pratap  Kumar Panda, Department of Chemistry, A.B College, Basudevpur, Bhadrak, Odisha, India. Email:


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.



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Fluoride Contents of Community Drinking Water: Biological and Public Health Implications

1Department of Integrated Science/Biology Education, Federal College of Education (Technical) Omoku, Nigeria

2Medical Director, Sick Bay, Federal College of Education (Technical) Omoku, Nigeria

American Journal of Water Resources. 2016, 4(3), 54-57
doi: 10.12691/ajwr-4-3-1
Copyright © 2016 Science and Education Publishing

Cite this paper:
Dirisu C.G, Mafiana M. O, Okwodu N.E, Isaac A.U.. Fluoride Contents of Community Drinking Water: Biological and Public Health Implications. American Journal of Water Resources. 2016; 4(3):54-57. doi: 10.12691/ajwr-4-3-1.

Correspondence to: Dirisu  C.G, Department of Integrated Science/Biology Education, Federal College of Education (Technical) Omoku, Nigeria. Email:


Drinking water supplies in Omoku, Nigeria was analyzed for its fluoride content investigated. For the analysis, twenty-four different water samples were systematically collected from stream, well water, public tap and private borehole in defined locations in Omoku. Level of fluoride was determined using standard method-Ion-Selective Electrode method. The mean values obtained were compared directly with the limit recommended by the World Health organization (WHO) of 1.0mg/L. Private borehole and public tap water had overall mean fluoride levels of 0.94±0.07 mg/L and 0.86±0.30 mg/L respectively, while stream had 0.95±0.09 mg/L and well water had the lowest level of fluoride (0.48±0.03 mg/L). Fluoride content of private borehole, public tap water and stream were approximately within the specified minimum limit of World health organization, while that of well water was below stipulated limit. ANOVA statistics indicated that there was no significant difference in the mean Fluoride levels of the water samples (p>0.05). Low levels of fluoride are associated with dental caries and hence such water should be fluoridated. Biological implications of low and high fluoride levels of water are highlighted in order to create awareness on the need to protect the general public from either dental caries or fluorosis of the teeth and/or skeleton respectively.



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