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The semi-distributed approaches proposed in the past for simulating rainfall–runoff (RR) still require extensive hydro-meteorological data for their complex calibration process. Therefore, research efforts are needed to develop novel and innovative semi-distributed RR models that require a minimum amount of data and effort. In this study, four semi-distributed RR models are proposed using a simple lumped model in a distributed sense by gradually enforcing spatial distribution in terms of hydro-meteorological and physiographical features in a basin. Only rainfall, runoff, and temperature data from three contrasting basins were employed in developing the proposed models. The results show that semi-distributed models performed better than the lumped model, and the accuracy increased with gradual enforcement of the spatial variations in various data. Further, the results suggest that incorporation of the basin's physiographic features is the most important aspect in developing efficient semi-distributed RR models followed by hydrological and climatic information in a basin. [ABSTRACT FROM AUTHOR]

Network Intrusion detection performances are highly affected by imbalance data problems due to the presence of less number of attack information in the dataset. Deep learning models are applied in existing methods to improve the efficiency that has limitations of overfitting problems. The Generative Adversarial Network (GAN) -- Bayesian optimization Multi-class Support Vector Machine (BMSVM) is proposed to overcome imbalance and overfitting problems in intrusion detection systems. The Min-Max Normalization method is applied to normalize the input data to reduce the differences in features. GAN model is applied to generate minority class to balance the data instances to train the model. The proposed GAN-BMSVM model is compared with the classical sampling method, optimization, and classifier in the intrusion detection model in terms of Accuracy, Detection Rate (DR), and False Alarm Rate (FAR). The classical sampling methods are Near-miss, SMOTE and Autoencoder; traditional classifiers are KNN, RF, SVM, DNN and LSTM, and classical optimizations are PSO and WOA. The existing researches such as HCRNN, HLD, DONN, FL-NIDS and CNN-LSTM are used to evaluate the efficiency of GAN-BMSVM model. The GAN-BMSVM model has achieved 99.58% and 85.38 % accuracy for NSL-KDD and UNSW-NB15 dataset respectively, which is higher than the existing CNN-LSTM model. [ABSTRACT FROM AUTHOR]