Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task. Keywords: flood prediction; flood forecasting; flash-flood model, big flood management; hydrologic model; rainfall-runoff, hybrid & ensemble machine learning; artificial neural networks (ANNs); support vector machines (SVM); natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision trees (DT); internet of things (IoT); random forest (RF); survey; classification and regression trees (CART), data science; deep learning; big data; bagging, boosting, artificial intelligence (AI); soft computing; extreme event management; time series prediction; multilayer perceptron (MLP); simulated annealing (SA); multivariate adaptive regression splines (MARS), supervised learning IntroductionAmong the natural disasters, floods are the most destructive, causing massive damage to human life, infrastructure, agriculture, and the socioeconomic system.Governments, therefore, are under pressure to develop reliable and accurate maps of flood risk areas and further plan for sustainable flood risk management focusing on prevention, protection, and preparedness [1]. Flood prediction models are of significant impor...

Abstract. Developing a hydrological forecasting model based on past records is crucial to 23 effective hydropower reservoir management and scheduling. Traditionally, time series analysis and 24 modeling is used for building mathematical models to generate hydrologic records in hydrology 25 and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of 26 analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to 27 apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive 28 moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive 29 neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and 30 support vector machine (SVM) method are examined using the long-term observations of monthly 31 river flow discharges. The four quantitative standard statistical performance evaluation measures, 32 the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (E), root mean squared 33 error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the 34 performances of various models developed. Two case study river sites are also provided to 35 illustrate their respective performances. The results indicate that the best performance can be 36 obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and 37 validation phases. 38 39

During the past two decades of the e-commerce growth the concept of business model has become increasingly popular. More recently, the research on this realm has grown rapidly with a diverse research activity covering a wide range of application areas. Considering the sustainable development goals the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural or other contexts in a sustainable way. The process of sustainable business model construction forms an innovative part of business strategy. Different industries and businesses have utilized sustainable business models' concept to satisfy their economic, environmental and social goals simultaneously. However, the success, popularity, and the progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success-has been reviewed and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is to provide an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.

Accurate time-and site-specific forecasts of streamflow and reservoir inflow are important in effective hydropower reservoir management and scheduling. Traditionally, autoregressive movingaverage (ARMA) models have been used in modelling water resource time series as a standard representation of stochastic time series. Recently, artificial neural network (ANN) approaches have been proven to be efficient when applied to hydrological prediction. In this paper, the support vector machine (SVM) is presented as a promising method for hydrological prediction. Over-fitting and local optimal solution are unlikely to occur with SVM, which implements the structural risk minimization principle rather than the empirical risk minimization principle. In order to identify appropriate parameters of the SVM prediction model, a shuffled complex evolution algorithm is performed through exponential transformation. The SVM prediction model is tested using the long-term observations of discharges of monthly river flow discharges in the Manwan Hydropower Scheme. Through the comparison of its performance with those of the ARMA and ANN models, it is demonstrated that SVM is a very potential candidate for the prediction of long-term discharges.Key words autoregressive moving-average (ARMA) models; long-term discharge prediction; neural networks; SCE-UA algorithm; support vector machine Utilisation de "support vector machines" pour la prévision de débit à long terme Résumé La gestion et la programmation efficaces d'un barrage hydroélectrique requièrent des prévisions précises, dans le temps et spécifiques pour chaque site, de débit de cours d'eau et de flux entrant dans le réservoir. Les modèles autorégressifs à moyenne mobile (ARMA) sont traditionnellement utilisés pour la modélisation de séries temporelles hydrologiques comme une représentation standard de séries temporelles stochastiques. Récemment des approches par réseaux de neurones artificiels (RNA) se sont révélées être efficaces pour la prévision hydrologique. Dans cet article, une "support vector machine" (SVM) est présentée comme une méthode prometteuse pour la prévision hydrologique. L'approche SVM devrait éviter le sur-apprentisage et les optima locaux, car elle s'appuie sur le principe de minimisation structurelle du risque plutôt que sur le principe de minimisation empirique du risque. Un algorithme "shuffled complex evolution" est utilisé via une transformation exponentielle pour identifier les paramètres appropriés du modèle SVM de prévision. Le modèle SVM de prévision est testé avec les longues séries d'observation des débits mensuels de cours d'eau du complexe hydroélectrique de Manwan. Il apparaît, par comparaison de ses performances avec celles de modèles ARMA et RNA, que l'approche SVM est très pertinente pour la prévision de débits à long terme.

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