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Flood modelling and forecasting can enhance our understanding of flood mechanisms and facilitate effective management of flood risk. Conventional flood hazard and risk assessments usually consider one driver at a time, whether it is ocean, fluvial or pluvial, without considering the compound nature of flood events. In this paper, we developed a novel approach for modelling and forecasting compound coastal-fluvial floods using a two-step framework. In step one, a hydrodynamic model is used to simulate floodwater propagation; while in step two, machine learning (ML) models are used to generate flood forecasts. The architecture of hydrodynamic-ML forecasting system incorporates a hydrodynamic model covering a specific domain, with individual ML models trained for each pixel. In total 7 ML models including: Support Vector Regression (SVR), Support Vector Machine (SVM), Radial Basis Function (RBF), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree (DT), and Artificial Neural Network (ANN) were applied in this study. Forecasting compound floods is achieved using two sets of inputs: timeseries of river discharges in the upstream fluvial section and downstream ocean water levels in the coastal areas. The accuracy of the flood forecasting system is demonstrated for Cork City, Ireland; and modelling performance was evaluated using several statistical tools. Results show that the proposed models can provide reliable estimates of flood inundation and associated water depths. Overall, the RBF model exhibits the best performance. Despite the complexity of compound multi-driver floods, this study shows that the coupled hydrodynamic-ML approach can forecast coastal-fluvial flood with limited hydraulic and hydrological input data. This system overcomes the limitations of traditional hydrodynamic model-based systems where trade-offs between the always competing numerical model accuracy and computational time prohibit the model to be used for short-term flood forecasting. Once trained, the ML component of the coupled system can perform flood forecasting in near real-time, potentially integrating into a flood early warning system. Accurate flood forecasting has a wide range of positive societal impacts, including improved flood preparedness, increased confidence, better resource allocation, reduced flood damage, and potentially even flood prevention. • Novel technique to improve compound coastal-fluvial flood modelling. • Flood modelling technique based on seven Machine Learning methods. • Approach is successfully implemented, and results are evaluated over Cork, Ireland. • The results demonstrate minimal quantitative errors for the proposed models. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions. • This paper studies the effects of AI on China's GTFP from internal-structural perspective. • GTFP is decomposed into three internal factors by the global Malmquist index. • AI has positive effect on GTFP through different impacts on internal factors. • Improving resource allocation efficiency is the most critical channel of AI on GTFP. • AI plays a more significant role in promoting GTFP in less developed areas. [ABSTRACT FROM AUTHOR]

Several cities in the developing world, of which the capital city of India, New Delhi, is an example, often experience air quality in which pollutant levels go way above the levels considered hazardous for human health. To bring down the air quality to within permissible limits quickly, the measures typically taken involve shutting down certain high-polluting activities for some time to enable the air quality to recover temporarily. This paper presents a first-ever model based on artificial neural networks to forecast the extent of reduction in air quality parameters that can be achieved and the time period within which a change can be experienced when the source of the emissions is cut off temporarily. The model is based on the extensive data on the extent of reduction in air quality parameters that occurred during the lockdown that was imposed during the COVID-19 pandemic. The non-linear autoregressive exogenous network-based model chosen for the purpose employs the hour since stopping of emissions, relative humidity, wind speed, wind direction, and ambient temperature as input parameters to predict the rate of change of PM 2.5 with respect to the concentration at the start of the stopping of the emissions. Air quality data from a key monitoring station in New Delhi was used to develop the model. The model predicted the rate of drop in PM 2.5 with an R and MSE of 0.0044 and 0.9736, respectively, while training and 0.0095 and 0.9583 while testing. The model was then tested with data from 19 other stations in New Delhi, and accuracy of the model was found to be exceptionally accurate, with the correlation between the measured and the predicted PM 2.5 levels ranging from 0.74 to 0.94 and the MSE ranging from 0.0110 to 1.0746. Thus, the model can be employed to determine the number of hours of temporary stoppage of emissions required for the PM 2.5 concentration to reach safe levels. The methodology of development of the model can be extrapolated to construct models tailored for use in other parts of the world as well. • A novel ANN -based model is reported to predict rate of drop of PM 2.5 • Model has been trained on extensive dataset from the covid lockdown in Delhi. • Duration and efficacy of emergency measures to reduce emissions can be estimated. • The model uses easy-to-measure meteorological parameters as inputs. • Model has been validated for 19 stations of various land-use types in Delhi. [ABSTRACT FROM AUTHOR]