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Prediction of water level is an important task for groundwater planning and management when the water balance consistently tends toward negative values. In Maheshwaram watershed situated in the Ranga Reddy District of Andhra Pradesh, groundwater is overexploited, and groundwater resources management requires complete understanding of the dynamic nature of groundwater flow. Yet, the dynamic nature of groundwater flow is continually changing in response to human and climatic stresses, and the groundwater system is too intricate, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are introduced into groundwater science as a powerful, flexible, statistical modeling technique to address complex pattern recognition problems. This study presents the comparison of two methods, i.e., feed-forward neural network (FFNN) trained with Levenberg-Marquardt (LM) algorithm compared with a fuzzy logic adaptive network-based fuzzy inference system (ANFIS) model for better accuracy of the estimation of the groundwater levels of the Maheshwaram watershed. The statistical indices used in the analysis were the root mean square error (RMSE), regression coefficient (R 2 ) and error variation (EV).The results show that FFNN-LM and ANFIS models provide better accuracy (RMSE = 4.45 and 4.94, respectively, R 2 is 93% for both models) for estimating groundwater levels well in advance for the above location.

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The oriental fruit fly Bactrocera dorsalis (Hendel) is a very serious pest of fruit trees, causing enormous economic losses globally. The present study examines the capability of an artificial neural network (ANN) with a Quasi-Newton (QN) algorithm to predict a fruit fly trap catch and compare the results with those of a traditional regression model. MATLAB 7.0 was used to develop ANN programming and the fortnightly measurement of 14 input variables (abiotic along with biotic variables) provided the database for analysing the ANN model. An input model using a total of 14 identified input nodes with a selected QN-ANN structure (14-25-20-1) gave an optimum result. In general, the present study showed that an ANN could be used to estimate fruit fly trap catch with enhanced accuracy (R 2 ¼ 0.92; root mean square error (RMSE) ¼ 23.75; Nash -Sutcliffe efficiencies ¼ 0.99) over traditional regression models (R 2 ¼ 0.76; RMSE ¼ 30.28; Nash -Sutcliffe efficiencies ¼ 0.76). This finding helps the region-specific fruit fly monitoring and management programmes that lack long-term historic data.