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

Internet of Things early flood warning system with ethology input and fuzzy logic

Flood is considered as a serious natural disaster in Asia. Flood has affected millions of people in Asia in the recent years including Malaysia and its neighboring countries. The severity of the problems resulted from flood has significantly affected the government in terms of economic and social. Information Communication Technology (ICT) can be utilized in addressing flood challenge by contributing in the aspects of early flood warning as well as alerting the affected community. Early flood warning systems face several challenges in terms of warning dissemination that is not timely, people centered, accessible and explainable. Thus, this study developed an Internet of Thing (IoT) early flood warning system (IEFWS) with ethological input using fuzzy logic in order to come up with a timely, precise and low cost flood warning system. The IEFWS of fuzzy logic application included several nature input data membership functions specifically temperature, humidity, rainfall intensity, water raise rate, sound, and motion indicators were all being updated on the internet simultaneously in less then 0:00:05 seconds. This study also included an ethological input data of fish by analyzing the behavior of sound and movement of fish as indicators to early warning before flood occurrence. The system was tested and evaluated in terms of timely and preciseness of it to update sensor data to the internet and apply fuzzy logic to intelligently alert flood warning. The results showed that the system was able to update ubiquitous data for a better monitoring system platform. In addition, the system is low cost and easy to handle. In conclusion, the IoT early flood warning system is timely and precise as the data are updated at a very minimum delay and it could easily monitor the changes of climate

Development of a new hybrid technique for estimating of relative uplift force in gravity dams based on whale optimization algorithm

A numerical model is developed in this study using the finite element method (FEM) to estimate relative total uplift force for different positions of holes of drainage gallery in the foundation of Guangzhao gravity dam, located in China. The data of the relative total uplift force generated for different input combinations using the FEM were used to develop machine learning (ML) models. A three-layer Artificial Neural Network (ANN) and a new hybrid model known as ANN-Whale Optimization Algorithm (ANN-WOA) were used for this purpose. The results showed that R2, RMSE, NSE, KGE and RE% for ANN-WOA model in estimation of the relative total uplift forces were 0.998, 0.021, 0.989, 0.964 and 3.3% respectively and those for ANN model were 0.980, 0.023, 0.982, 0.953 and 4.67% respectively, which indicate the higher accuracy of ANN-WOA model compared to ANN model. The new hybrid model, ANN-WOA with the less RMSE and RE% and high KGE and NSE is a more appropriate model for the estimation of the relative total uplift force. The extracted metrics of violin plots indicated that the probability distribution of the relative total uplift force estimated using ANN-WOA model was very similar to that obtained using the FEM

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels

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

SIMULATING SEISMIC WAVE PROPAGATION IN TWO-DIMENSIONAL MEDIA USING DISCONTINUOUS SPECTRAL ELEMENT METHODS

We introduce a discontinuous spectral element method for simulating seismic wave in 2- dimensional elastic media. The methods combine the flexibility of a discontinuous finite element method with the accuracy of a spectral method. The elastodynamic equations are discretized using high-degree of Lagrange interpolants and integration over an element is accomplished based upon the Gauss-Lobatto-Legendre integration rule. This combination of discretization and integration results in a diagonal mass matrix and the use of discontinuous finite element method makes the calculation can be done locally in each element. Thus, the algorithm is simplified drastically. We validated the results of one-dimensional problem by comparing them with finite-difference time-domain method and exact solution. The comparisons show excellent agreement