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Biochemical oxygen demand (BOD) has been shown to be an important variable in water quality management and planning. However, BOD is difficult to measure and needs longer time periods (5 days) to get results. Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resource variables. The objective of this research was to develop an ANNs model to estimate daily BOD in the inlet of wastewater biochemical treatment plants. The plantscale data set (364 daily records of the year 2005) was obtained from a local wastewater treatment plant. Various combinations of daily water quality data, namely chemical oxygen demand (COD), water discharge (Q w ), suspended solid (SS), total nitrogen (N), and total phosphorus (P) are used as inputs into the ANN so as to evaluate the degree of effect of each of these variables on the daily inlet BOD. The results of the ANN model are compared with the multiple linear regression model (MLR). Mean square error, average absolute relative error, and coefficient of determination statistics are used as comparison criteria for the evaluation of the model performance. The ANN technique whose inputs are COD, Q w , SS, N, and P gave mean square errors of 708.01, average absolute relative errors of 10.03%, and a coefficient of determination 0.919, respectively. On the basis of the comparisons, it was found that the ANN model could be employed successfully in estimating the daily BOD in the inlet of wastewater biochemical treatment plants.

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Abstract.The results of the application of an unsupervised learning (neural network) approach comprising a Self Organizing Map (SOM), to distinguish micro-earthquakes from quarry blasts in the vicinity of Istanbul, Turkey, are presented and discussed. The SOM is constructed as a neural classifier and complementary reliability estimator to distinguish seismic events, and was employed for varying map sizes. Input parameters consisting of frequency and time domain data (complexity, spectral ratio, S/P wave amplitude peak ratio and origin time of events) extracted from the vertical components of digital seismograms were estimated as discriminants for 179 (1.8 < M d < 3.0) local events. The results show that complexity and amplitude peak ratio parameters of the observed velocity seismogram may suffice for a reliable discrimination, while origin time and spectral ratio were found to be fuzzy and misleading classifiers for this problem. The SOM discussed here achieved a discrimination reliability that could be employed routinely in observatory practice; however, about 6% of all events were classified as ambiguous cases. This approach was developed independently for this particular classification, but it could be applied to different earthquake regions.