Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

Environmental streamflow management can improve the ecological health of streams by returning modified flows to more natural conditions. The Ecological Limits of Hydrologic Alteration (ELOHA) framework for developing regional environmental flow criteria has been implemented to reverse hydromodification across the heterogenous region of coastal southern California (So. CA) by focusing on two elements of the flow regime: streamflow permanence and flashiness. Within ELOHA, classification groups streams by hydrologic and geomorphic similarity to stratify flow-ecology relationships. Analogous grouping techniques are used by hydrologic modelers to facilitate streamflow prediction in ungaged basins (PUB) through regionalization. Most watersheds, including those needed for stream classification and environmental flow development, are ungaged. Furthermore, So. CA is a highly heterogeneous region spanning a gradient of urbanization, which presents a challenge for regionalizing ungaged basins. In this study, we develop a novel classification technique for PUB modeling that uses an inductive approach to group regional streams by modeled hydrologic similarity followed by deductively determining class membership with hydrologic model errors and watershed metrics. As a new type of classification, this “Hydrologic Model-based Classification” (HMC) prioritizes modeling accuracy, which in turn provides a means to improve model predictions in ungaged basins, while complementing traditional classifications and improving environmental flow management. HMC is developed by calibrating a regional catalog of process-based rainfall-runoff models, quantifying the hydrologic reciprocity of calibrated parameters that would be unknown in ungaged basins, and grouping sites according to hydrologic and physical similarity. HMC was applied to 25 USGS streamflow gages in the south coast region of California and was compared to other hybrid PUB approaches combining inductive and deductive classification. Using an Average Cluster Error metric, results show HMC provided the most hydrologically similar groups according to calibrated parameter reciprocity. Hydrologic Model-based Classification is relatively complex and time-consuming to implement, but it shows potential for advancing ungaged basin management. This study demonstrates the benefits of thorough stream classification using multiple approaches, and suggests that Hydrologic Model-based Classification has advantages for PUB and building the hydrologic foundation for environmental flow management.

Improvement of Data Stream Decision Trees

The classification of data streams has become a significant and active research area. The principal characteristics of data streams are a large amount of arrival data, the high speed and rate of its arrival, and the change of their nature and distribution over time. Hoeffding Tree is a method to, incrementally, build decision trees. Since its proposition in the literature, it has become one of the most popular tools of data stream classification. Several improvements have since emerged. Hoeffding Anytime Tree was recently introduced and is considered one of the most promising algorithms. It offers a higher accuracy compared to the Hoeffding Tree in most scenarios, at a small additional computational cost. In this work, the authors contribute by proposing three improvements to the Hoeffding Anytime Tree. The improvements are tested on known benchmark datasets. The experimental results show that two of the proposed variants make better usage of Hoeffding Anytime Tree’s properties. They learn faster while providing the same desired accuracy.

Stream Classification Algorithm Based on Decision Tree

Due to the rise of many fields such as e-commerce platforms, a large number of stream data has emerged. The incomplete labeling problem and concept drift problem of these data pose a huge challenge to the existing stream data classification methods. In this respect, a dynamic stream data classification algorithm is proposed for the stream data. For the incomplete labeling problem, this method introduces randomization and iterative strategy based on the very fast decision tree VFDT algorithm to design an iterative integration algorithm, and the algorithm uses the previous model classification result as the next model input and implements the voting mechanism for new data classification. At the same time, the window mechanism is used to store data and calculate the data distribution characteristics in the window, then, combined with the calculated result and the predicted amount of data to adjust the size of the sliding window. Experiments show the superiority of the algorithm in classification accuracy. The aim of the study is to compare different algorithms to evaluate whether classification model adapts to the current data environment.

Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification

Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.

Cost-Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance

Class imbalance and concept drift are two primary principles that exist concurrently in data stream classification. Although the two issues have drawn enough attention separately, the joint treatment largely remains unexplored. Moreover, the class imbalance issue is further complicated if data streams with concept drift. A novel Cost-Sensitive based Data Stream (CSDS) classification is introduced to overcome the two issues simultaneously. The CSDS considers cost information during the procedures of data preprocessing and classification. During the data preprocessing, a cost-sensitive learning strategy is introduced into the ReliefF algorithm for alleviating the class imbalance at the data level. In the classification process, a cost-sensitive weighting schema is devised to enhance the overall performance of the ensemble. Besides, a change detection mechanism is embedded in our algorithm, which guarantees that an ensemble can capture and react to drift promptly. Experimental results validate that our method can obtain better classification results under different imbalanced concept drifting data stream scenarios.