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Abstract:Dissolved oxygen mass balance has been computed for different reaches of River Kali in western Uttar Pradesh (India) to obtain the reaeration coefficient K 2 . A total of 270 field data sets have been collected during the period from March 1999 to February 2000. Eleven most popular predictive equations, used for reaeration prediction and utilizing mean stream velocity, bed slope, flow depth, friction velocity and Froude number, have been tested for their applicability in the River Kali using data generated during field survey. The K 2 values computed from these predictive equations have been compared with the K 2 values observed from dissolved oxygen balance measurements in the field. The performance of predictive equations have been evaluated using error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and correlation statistics. The equations developed by Smoot and by Cadwallader and McDonnell showed comparatively better results. Moreover, a refined predictive equation has been developed using a least-squares algorithm for the River Kali that minimizes error estimates and improves correlation between observed and computed reaeration coefficients.

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Internet, a client/server system, is a perfect means of GIS data accessing, analyzing and transmission. The World Wide Web, FTP (file transfer protocol) and HTTP programs make it convenient to access and transfer data files across the Internet. Using Internet for GIS makes it easy access to acquire GIS data from diverse data sources in the distributed environment. The geospatial multicriteria decision analysis in a client/server environment is an important and challenging task for the GIS community because of narrow Internet bandwidth for large geospatial data sets. In the present paper, we are developing a multicriteria decision analysis tool for spatial decision making in the web GIS environment. The developed system has been demonstrated for biodiversity conservation and priorities. An attempt has been made to generate the alternative decisions based on priority vectors. The multicriteria technique of Analytic Hierarchy Process (AHP) is used to derive the eigen vectors with the given multiple constraints of conflicting criteria and aims at selecting optimal alternative from the available sets. However, the evaluation recognizes the importance of expert knowledge when assigning the weights for the best spatial priorities. Comparing within classes and alternatives using judgment and decision matrix is Geoinformatica (

Abstract:This paper compares artificial neural network (ANN), fuzzy logic (FL) and linear transfer function (LTF)-based approaches for daily rainfall-runoff modelling. This study also investigates the potential of Takagi-Sugeno (TS) fuzzy model and the impact of antecedent soil moisture conditions in the performance of the daily rainfall-runoff models. Eleven different input vectors under four classes, i.e. (i) rainfall, (ii) rainfall and antecedent moisture content, (iii) rainfall and runoff and (iv) rainfall, runoff and antecedent moisture content are considered for examining the effects of input data vector on rainfall-runoff modelling. Using the rainfall-runoff data of the upper Narmada basin, Central India, a suitable modelling technique with appropriate model input structure is suggested on the basis of various model performance indices. The results show that the fuzzy modelling approach is uniformly outperforming the LTF and also always superior to the ANN-based models.

Abstract:Different commonly used predictive equations for the reaeration rate coefficient (K 2 ) have been evaluated using 231 data sets obtained from the literature and 576 data sets measured at different reaches of the River Kali in western Uttar Pradesh, India. The data sets include stream/channel velocity, bed slope, flow depth, cross-sectional area and reaeration rate coefficient (K 2 ), obtained from the literature and generated during the field survey of River Kali, and were used to test the applicability of the predictive equations. The K 2 values computed from the predictive equations have been compared with the corresponding K 2 values measured in streams/channels. The performance of the predictive equations has been evaluated using different error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and coefficient of determination (r 2 ). The results show that the reaeration rate equation developed by Parkhurst and Pomeroy yielded the best agreement, with the values of SE, NME, MME and r 2 as 33Ð387, 4Ð62, 3Ð58 and 0Ð95, respectively, for literature data sets (case 1) and 37Ð567, 3Ð57, 2Ð6 and 0Ð95, respectively, for all the data sets (literature data sets and River Kali data sets) (case 2). Further, to minimize error estimates and improve correlation between measured and computed reaeration rate coefficients, supplementary predictive equations have been developed based on Froude number criteria and a least-squares algorithm. The supplementary predictive equations have been verified using different error estimates and by comparing measured and computed reaeration rate coefficients for data sets not used in the development of the equations.

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