Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Hybrid machine learning model based on feature decomposition and entropy optimization for higher accuracy flood forecasting

The advancement of machine learning model has widely been adopted to provide flood forecast. However, the model must deal with the challenges to determine the most important features to be used in in flood forecast with high-dimensional non-linear time series when involving data from various stations. Decomposition of time-series data such as empirical mode decomposition, ensemble empirical mode decomposition and discrete wavelet transform are widely used for optimization of input; however, they have been done for single dimension time-series data which are unable to determine relationships between data in high dimensional time series.  In this study, hybrid machine learning models are developed based on this feature decomposition to forecast the monthly water level using monthly rainfall data. Rainfall data from eight stations in Kelantan River Basin are used in the hybrid model. To effectively select the best rainfall data from the multi-stations that provide higher accuracy, these rainfall data are analyzed with entropy called Mutual Information that measure the uncertainty of random variables from various stations. Mutual Information act as optimization method helps the researcher to select the appropriate features to score higher accuracy of the model. The experimental evaluations proved that the hybrid machine learning model based on the feature decomposition and ranked by Mutual Information can increase the accuracy of water level forecasting.  This outcome will help the authorities in managing the risk of flood and helping people in the evacuation process as an early warning can be assigned and disseminate to the citizen

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

Groundwater level prediction using a multiple objective genetic algorithm-grey relational analysis based weighted ensemble of anfis models

Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the Adaptive Neuro Fuzzy Inference System (ANFIS). These models included Differential Evolution-ANFIS (DE-ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), and traditional Hybrid Algorithm tuned ANFIS (HA-ANFIS) for the one-and multi-week forward forecast of groundwater levels at three observation wells. Model-independent partial autocorrelation functions followed by frequentist lasso regression-based feature selection approaches were used to recognize appropriate input variables for the prediction models. The performances of the ANFIS models were evaluated using various statistical performance evaluation indexes. The results revealed that the optimized ANFIS models performed equally well in predicting one-week-ahead groundwater levels at the observation wells when a set of various performance evaluation indexes were used. For improving prediction accuracy, a weighted-average ensemble of ANFIS models was proposed, in which weights for the individual ANFIS models were calculated using a Multiple Objective Genetic Algorithm (MOGA). The MOGA accounts for a set of benefits (higher values indicate better model performance) and cost (smaller values indicate better model performance) performance indexes calculated on the test dataset. Grey relational analysis was used to select the best solution from a set of feasible solutions produced by a MOGA. A MOGA-based individual model ranking revealed the superiority of DE-ANFIS (weight = 0.827), HA-ANFIS (weight = 0.524), and HAANFIS (weight = 0.697) at observation wells GT8194046, GT8194048, and GT8194049, respectively. Shannon’s entropy-based decision theory was utilized to rank the ensemble and individual ANFIS models using a set of performance indexes. The ranking result indicated that the ensemble model outperformed all individual models at all observation wells (ranking value = 0.987, 0.985, and 0.995 at observation wells GT8194046, GT8194048, and GT8194049, respectively). The worst performers were PSO-ANFIS (ranking value = 0.845), PSO-ANFIS (ranking value = 0.819), and DE-ANFIS (ranking value = 0.900) at observation wells GT8194046, GT8194048, and GT8194049, respectively. The generalization capability of the proposed ensemble modelling approach was evaluated for forecasting 2-, 4-, 6-, and 8-weeks ahead groundwater levels using data from GT8194046. The evaluation results confirmed the useability of the ensemble modelling for forecasting groundwater levels at higher forecasting horizons. The study demonstrated that the ensemble approach may be successfully used to predict multi-week-ahead groundwater levels, utilizing previous lagged groundwater levels as inputs

Assessment of the Regionalization of Precipitation in Two Canadian Climate Regions: A Fuzzy Clustering Approach

Regional frequency analysis (RFA) is used to obtain reliable estimates of local precipitation events for a variety of applications in water resources engineering. The focus of the presented research is on an initial step of the RFA process; that is the formation of precipitation regions (also referred to as regionalization). The aim of this study is to dissect the regionalization procedure into its individual components that require subjective user input, and to evaluate their respective influences on the results. All assessments are conducted in two of Canada\u27s climate regions; namely the Prairie and Great Lakes-St. Lawrence lowlands. Additionally, a new fuzzy clustering approach to regionalization that uses optimization is proposed. It is evident that the outcomes are sensitive to the choice of the regionalization method, the number of regions into which the sites of the study area are partitioned, the climate site attributes and the temporal resolution of the precipitation data. Recommendations for the selection of such factors are provided based on their application

Identification of Flash floods using Soil Flux and CO2: An implementation of Neural Network with Less False Alarm Rate

Flash floods are very sudden and abrupt and are the major root cause of casualties and loss of infrastructure. Flash floods can be regarded as the topmost natural disasters in many countries. Usually floods are due to high precipitation, wind velocity, water wave current and melting of ice bergs. Diversified strategies have been designed and applied to identify the flash floods. Mainly dozen of sensors have been utilized to detect the flash floods like upstream level, rainfall intensity, run-off magnitude, run-off speed, color of the water, precipitation velocity, pressure, temperature, wind speed, wave current pattern and cloud to ground (CG flashes). Ultrasonic and passive infrared (PIR) sensors have also been utilized for this purpose. Sensors generate high amount of fake alerts due to the incompetent algorithms. In our research we have proposed a novel approach analysis of soil flux depicting atmospheric carbon dioxide level as the plants take smaller amount of water from the soil due to the heightened levels of carbon dioxide. Due to this newly discovered research the soil is saturated abruptly causes more floods and run-offs. In our research we have reduced the false alarms and reduced the false alarms by using scaled conjugate gradient back propagation. Simulation results showed that scaled conjugate gradient propagation performed better than the other previous methods

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events

Data Mining

Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment

Hybrid machine learning model based on feature decomposition and entropy optimization for higher accuracy flood forecasting

The advancement of machine learning model has widely been adopted to provide flood forecast. However, the model must deal with the challenges to determine the most important features to be used in in flood forecast with high-dimensional non-linear time series when involving data from various stations. Decomposition of time-series data such as empirical mode decomposition, ensemble empirical mode decomposition and discrete wavelet transform are widely used for optimization of input; however, they have been done for single dimension time-series data which are unable to determine relationships between data in high dimensional time series.  In this study, hybrid machine learning models are developed based on this feature decomposition to forecast the monthly water level using monthly rainfall data. Rainfall data from eight stations in Kelantan River Basin are used in the hybrid model. To effectively select the best rainfall data from the multi-stations that provide higher accuracy, these rainfall data are analyzed with entropy called Mutual Information that measure the uncertainty of random variables from various stations. Mutual Information act as optimization method helps the researcher to select the appropriate features to score higher accuracy of the model. The experimental evaluations proved that the hybrid machine learning model based on the feature decomposition and ranked by Mutual Information can increase the accuracy of water level forecasting.  This outcome will help the authorities in managing the risk of flood and helping people in the evacuation process as an early warning can be assigned and disseminate to the citizen