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A prompt and precise estimation of traffic conditions on the scale of a few minutes by analyzing past data is crucial for establishing an effective intelligent traffic management system. Nevertheless, because of the irregularity and nonlinear features of traffic flow data, developing a prediction model with excellent robustness poses a significant obstacle. Therefore, we propose genetic-search-algorithm-improved kernel extreme learning machine, termed GA-KELM, to unleash the potential of improved prediction accuracy and generalization performance. By substituting the inner product with a kernel function, the accuracy of short-term traffic flow forecasting using extreme learning machines is enhanced. The genetic algorithm evades manual traversal of all possible parameters in searching for the optimal solution. The prediction performance of GA-KELM is evaluated on eleven benchmark datasets and compared with several state-of-the-art models. There are four benchmark datasets from the A1, A2, A4, and A8 highways near the ring road of Amsterdam, and the others are D1, D2, D3, D4, D5, D6, and P, close to Heathrow airport on the M25 expressway. On A1, A2, A4, and A8, the RMSEs of the GA-KELM model are 284.67 vehs/h, 193.83 vehs/h, 220.89 vehs/h, and 163.02 vehs/h, respectively, while the MAPEs of the GA-KELM model are 11.67%, 9.83%, 11.31%, and 12.59%, respectively. The results illustrate that the GA-KELM model is obviously superior to state-of-the-art models. [ABSTRACT FROM AUTHOR]

The water ecological environment problems brought about by rapid urbanization have prompted the proposal and implementation of different approaches to urban water ecological construction, such as eco-cities, best management practices (BMPs), and low-impact development (LID). As one of the most representative urban water ecological management policies in China, the Water Ecological Civilization City (WECC) was proposed in 2013, and 105 cities were selected for pilot construction. Many studies have evaluated the effectiveness of WECC construction, but international quantitative comparison is lacking. To address this, an urban Water-Human-Health (WHH) Assessment Model, considering water resources, ecological environment, economic and social development level, and water resources utilization, was developed and applied to five WECC pilot cities in China and 10 other cities worldwide, in which mainstream urban water ecological construction modes have been used. Principal component analysis of the index values in the assessment system was used to evaluate the current status of water ecosystem health in the 15 cities, showing that Sydney, Cleveland, and Hamburg were the most advanced in urban water ecological management. The two cities with the best evaluation results (Sydney and Cleveland), and the WECC city with the highest score (Wuhan) were selected for documentary analysis of their water ecological construction documents to identify similarities and differences to inform best practice internationally for urban water ecological construction. The results showed that Sydney and Cleveland attach similar emphasis across most constituents of urban water ecological construction, while, for Wuhan, greater importance is attached to water resource management and water culture. The advantages and disadvantages of WECC construction and international experience are discussed. The WHH assessment model proposed in this study provides a new quantitative evaluation method for international urban water ecological health evaluation, which could be further improved by including an urban flood risk indicator. [ABSTRACT FROM AUTHOR]

Excessive nitrogen (N) and phosphorus (P) input resulting from anthropogenic activities seriously threatens the supply security of drinking water sources. Assessing nutrient input and export as well as retention risks is critical to ensuring the quality and safety of drinking water sources. Conventional balance methods for nutrient estimation rely on statistical data and a huge number of estimation coefficients, which introduces uncertainty into the model results. This study aimed to propose a convenient, reliable, and accurate nutrient prediction model to evaluate the potential nutrient retention risks of drinking water sources and reduce the uncertainty inherent in the traditional balance model. The spatial distribution of pollutants was characterized using time-series satellite images. By embedding human activity indicators, machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), were constructed to estimate the input and export of nutrients. We demonstrated the proposed model's potential using a case study in the Yanghe Reservoir Basin in the North China Plain. The results indicate that the area information concerning pollution source types was effectively established based on a multi-temporal fusion method and the RF classification algorithm, and the overall classification low-end accuracy was 92%. The SVM model was found to be the best in terms of predicting nutrient input and export. The determination coefficient (R2) and Root Mean Square Error (RMSE) of N input, P input, N export, and P export were 0.95, 0.94, 0.91, and 0.93, respectively, and 32.75, 5.18, 1.45, and 0.18, respectively. The low export ratios (2.8–3.0% and 1.1–2.2%) of N and P, the ratio of export to input, further confirmed that more than 97% and 98% of N and P, respectively, were retained in the watershed, which poses a pollution risk to the soil and the quality of drinking water sources. This nutrient prediction model is able to improve the accuracy of non-point source pollution risk assessment and provide useful information for water environment management in drinking water source regions. [ABSTRACT FROM AUTHOR]

As the most direct indicator of drought, the dynamic assessment and prediction of actual evapotranspiration (AET) is crucial to regional water resources management. This research aims to develop a framework for the regional AET evaluation and prediction based on multiple machine learning methods and multi-source remote sensing data, which combines Boruta algorithm, Random Forest (RF), and Support Vector Regression (SVR) models, employing datasets from CRU, GLDAS, MODIS, GRACE (-FO), and CMIP6, covering meteorological, vegetation, and hydrological variables. To verify the framework, it is applied to grids of South America (SA) as a case. The results meticulously demonstrate the tendency of AET and identify the decisive role of T, P, and NDVI on AET in SA. Regarding the projection, RF has better performance in different input strategies in SA. According to the accuracy of RF and SVR on the pixel scale, the AET prediction dataset is generated by integrating the optimal results of the two models. By using multiple parameter inputs and two models to jointly obtain the optimal output, the results become more reasonable and accurate. The framework can systematically and comprehensively evaluate and forecast AET; although prediction products generated in SA cannot calibrate relevant parameters, it provides a quite valuable reference for regional drought warning and water allocating. [ABSTRACT FROM AUTHOR]

A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model's performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model. [ABSTRACT FROM AUTHOR]

Many studies have found that damming a river can change downstream hydrology, sediment transport, channel morphology, and fish habitat. However, little is known about river dam effects on downstream riparian wetland dynamics and their quantitative relationship with hydrological alterations. In this study, hydrological time series and wetland distribution data spanning nearly 40 years (1978–2016) before and after the construction of a large dam in 2005 across the Nenjiang River in Northeast China were used to reveal the impact of dam on the downstream discharge regime and wetland degradation. Hydro-statistical and stepwise multiple regression analyses were performed to quantify the relationship of riparian wetland area with a metrics of 33 hydrological indicators. Dam construction caused decline in peak discharge, flood frequency, and magnitude. Moreover, 150 km riparian wetlands along the downstream of the dam was largely reduced. The count and duration of high flow pulses, 1-day maximum, and date of maximum discharge changed significantly after the dam construction. The hydrological changes have made a significant contribution to the 44% reduction in riparian wetlands following the dam construction. Our results indicated that hydrological alterations caused by dam regulation led to the area reduction of downstream riparian wetlands. The findings provide relevant information for developing best dam operation practices to protect and restore downstream wetland ecosystems. [ABSTRACT FROM AUTHOR]