Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin

The conditions which affect the sustainability of water cause a number of serious environmental and hydrological problems. Effective and correct management of water resources constitutes an effective and important issue among scales. In this sense, a precise estimation of streamflow time series in rivers is one of the most important issues in optimal management of surface water resources. Therefore, a hybrid method combining particle swarm algorithm (PSO) and long short-term memory networks (LSTM) are proposed to predict flow with data obtained from different flow measurement stations. In this respect, the data gathered from three Flow Measurement Stations (FMS) from Zamanti and Eglence rivers located on Seyhan Basin are utilized. Besides, the proposed LSTM-PSO method is compared to an adaptive neuro-fuzzy inference system (ANFIS) and the LSTM benchmark model to demonstrate the performance achievement of proposed method. The prediction performances of the developed hybrid model and the others are tested on the determined stations. The forecasting performances of the models are determined with RMSE, MAE, MAPE, SD, and R-2 metrics. The comparison results indicated that the LSTM-PSO method provides highest results with values of R-2 (approximate to 0.9433), R-2 (approximate

A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM) is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM) model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds) and an approximation (A3) at three resolution levels (21-22-23) using Daubechies (db3) discrete wavelet. Correlation coefficients between each D subtime series and original monthly streamflow time series are calculated. Ds components with high correlation coefficients (D3) are added to the approximation (A3) as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters, C, ε, and σ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed

Data-driven models for monthly streamflow time series prediction

Author name used in this publication: K.W. Chau2010-2011 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

The community’s well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models’ concepts and historical uses would be beneficial in preventing researchers from overlooking models’ potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the “state of the art” on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing-based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables

A Systematic Review of Real-time Urban Flood Forecasting Model in Malaysia and Indonesia -Current Modelling and Challenge

Several metropolitan areas in tropical Southeast Asia, mainly in Malaysia and Indonesia have lately been witnessing unprecedentedly severe flash floods owing to unexpected climate change. The fast water flooding has caused extraordinarily serious harm to urban populations and social facilities. In addition, urban Southeast Asia generally has insufficient capacity in drainage systems, complex land use patterns, and a largely susceptible population in confined urban regions. To lower the urban flood risk and strengthen the resilience of vulnerable urban populations, it has been of fundamental relevance to create real-time urban flood forecasting systems for flood disaster prevention agencies and the urban public. This review examined the state-of-the-art models of real-time forecasting systems for urban flash floods in Malaysia and Indonesia. The real-time system primarily comprises the following subsystems, i.e., rainfall forecasting, drainage system modeling, and inundation area mapping. This review described the current urban flood forecasting modeling for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. The analysis found that urban flood forecasting modeling based on data-driven AI models is the most applied in many metropolitan locations in Malaysia and Indonesia. The analysis also evaluated the existing potential of data-driven AI models for real-time forecasting systems as well as the challenges towards i

Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-processing

Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models