Machine Learning Methods for Better Water Quality Prediction

In any aquatic system analysis, the modelling water quality parameters are of considerable significance. The traditional modelling methodologies are dependent on datasets that involve large amount of unknown or unspecified input data and generally consist of time-consuming processes. The implementation of artificial intelligence (AI) leads to a flexible mathematical structure that has the capability to identify non-linear and complex relationships between input and output data. There has been a major degradation of the Johor River Basin because of several developmental and human activities. Therefore, setting up of a water quality prediction model for better water resource management is of critical importance and will serve as a powerful tool. The different modelling approaches that have been implemented include: Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Networks (RBF-ANN), and Multi-Layer Perceptron Neural Networks (MLP-ANN). However, data obtained from monitoring stations and experiments are possibly polluted by noise signals as a result of random and systematic errors. Due to the presence of noise in the data, it is relatively difficult to make an accurate prediction. Hence, a Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter. In the domain of interests, the water quality parameters primarily include ammoniacal nitrogen (AN), suspended solid (SS) and pH. In order to evaluate the impacts on the model, three evaluation techniques or assessment processes have been used. The first assessment process is dependent on the partitioning of the neural network connection weights that ascertains the significance of every input parameter in the network. On the other hand, the second and third assessment processes ascertain the most effectual input that has the potential to construct the models using a single and a combination of parameters, respectively. During these processes, two scenarios were introduced: Scenario 1 and Scenario 2. Scenario 1 constructs a prediction model for water quality parameters at every station, while Scenario 2 develops a prediction model on the basis of the value of the same parameter at the previous station (upstream). Both the scenarios are based on the value of the twelve input parameters. The field data from 2009 to 2010 was used to validate WDT-ANFIS. The WDT-ANFIS model exhibited a significant improvement in predicting accuracy for all the water quality parameters and outperformed all the recommended models. Also, the performance of Scenario 2 was observed to be more adequate than Scenario 1, with substantial improvement in the range of 0.5% to 5% for all the water quality parameters at all stations. On validating the recommended model, it was found that the model satisfactorily predicted all the water quality parameters (R2 values equal or bigger than 0.9). © 201

Water Quality Prediction Based on Machine Learning Techniques

Water is one of the most important natural resources for all living organisms on earth. The monitoring of treated wastewater discharge quality is vitally important for the stability and protection of the ecosystem. Collecting and analyzing water samples in the laboratory consumes much time and resources. In the last decade, many machine learning techniques, like multivariate linear regression (MLR) and artificial neural network (ANN) model, have been proposed to address the problem. However, simple linear regression analysis cannot accurately forecast water quality because of complicated linear and nonlinear relationships in the water quality dataset. The ANN model also has shortcomings though it can accurately predict water quality in some scenarios. For example, ANN models are unable to formulate the non-linear relationship hidden in the dataset when the input parameters are ambiguous, which is common in water quality dataset. The adaptive neuro-fuzzy inference system (ANFIS) has been proven to be an effective tool in formulating the complicated linear and non-linear relationship hidden in datasets. Although the ANFIS model can achieve good performance in the water quality prediction, it has some limitations. Firstly, the size of the training dataset should not be less than the number of training parameters required in the model. Secondly, when the data distribution in the testing dataset is not reflected in the training dataset, the ANFIS model may generate out-of-range errors. Lastly, a strong correlation is required between input and target parameters. If the correlation is weak, the ANFIS model cannot accurately formulate the hidden relationship. In this dissertation, several methods have been proposed to improve the performance of ANFIS-based water quality prediction models. Stratified sampling is employed to cover different kinds of data distribution in the training and testing datasets. The wavelet denoising technique is iv used to remove the noise hidden in the dataset. A deep prediction performance comparison between MLR, ANN, and ANFIS model is presented after stratified sampling and wavelet denoising techniques are applied. Because water quality data can be thought as a time series dataset, a time series analysis method is integrated with the ANFIS model to improve prediction performance. Lastly, intelligence algorithms are used to optimize the parameters of membership functions in the ANFIS model to promote the prediction accuracy. Experiments based on water quality datasets collected from Las Vegas Wash since 2007 and Boulder Basin of Lake Mead, Nevada, between 2011 and 2016 are used to evaluate the proposed models

IoT technology for smart Water system

A serious drop in ensuring the water quality in the distribution system is a factor that affects public health. This could lead to increase in biological and non-biological contents, change in colour and odour of the water. These contaminants cause a serious threat to the whole water ecosystem. The conventional methods of analyzing the water quality require much time and labour. So there is a need to monitor and protect the water with a real time water quality monitoring system in order to make active measurements to reduce contamination. The growth of the technology had helped in developing efficient methods to solve many serious issues in real-time. Internet of things (IoT) has achieved a great focus due to its faster processing and intelligence. This paper focuses on discussing the architecture, applications and need of IoT in water management syste

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Multi-Agent Modeling for Integrated Process Planning and Scheduling

Multi-agent systems have been used for modelling various problems in the social, biological and technical domain. When comes to technical systems, especially manufacturing systems, agents are most often applied in optimization and scheduling problems. Traditionally, scheduling is done after creation of process plans. In this paper, agent methodology is used for integration of these two functions. The proposed multi-agent architecture provides simultaneous performance of process planning and scheduling and it consists of four intelligent agents: part and job agents, machine agent, and optimization agent. Verification and feasibility of a proposed approach is conducted using agent based simulation in AnyLogic software

A fundamental investigation and ultrasonic characterisation of coal effective stress behaviour

Coal seam gas is an important energy resource worldwide. The methane extraction from coal seam is often accompanied by CO2 injection to enhance the gas recovery and reduce the greenhouse gas emission footprint. Amongst all processes active in coal seam gas extraction, understanding and characterizing the coal effective stress and its evolution with time need special attention. The reservoir characteristics of coal seam that control its effective stress evolution, however, differ from that of other hydrocarbon resources in granular sediments. In conventional reservoirs, it is the pore volume determining the gas storage, but in coal seams, it is the pore surface area that defines the capacity through adsorption. As naturally fractured reservoirs, coal seams often contain an extensive fracture network called cleats thus, the coupled relationship between the mechanical behaviour and sorption effect can significantly influence the effective stress and in turn permeability. The main aim of this dissertation is thus to investigate and model the interaction of effective stress evolution and adsorption/desorption processes using advanced and innovative laboratory experiments and numerical simulations. This dissertation includes four parts consisting of the modelling and fundamental studies employing novel experimental and numerical techniques. The first part of the dissertation seeks to understand the characteristics of sorption-hydromechanical behaviour through the microscale and non-destructive investigation using micro-computed tomography (μCT), ultrasonic and their combined measurements. These fundamental investigations shed light on the coupled physical processes in different coal samples extracted from the Sydney Basin Australia through tracking 3D internal geometry. Using an X-ray transparent triaxial system, a range of stress-pore pressure boundary conditions are applied on different coals to obtain the 3D internal structure. The different coal components and their fracture patterns are analysed with respect to the bulk, matrix, and fracture compressibilities. In a further step, the coupled stress and swelling strain responses of coal when exposed to carbon dioxide (CO2) and helium (He) are studied by imbedding ultrasonic sensors in the X-ray transparent triaxial system. With the real-time visualisation of fracture porosity due to different adsorption levels, the effects of CO2 adsorption and involved processes on ultrasonic responses are investigated. The combined physical and numerical methods determine the main factor influencing wave propagation in coal to i) assist developing the representative constitutive model and ii) be used in the acoustic-driven parameterisation in the final part of the dissertation. The micro-scale investigation highlights the importance of internal structure affecting the mechanical properties in coal and the acoustic wave velocity allows evaluating the changes in facture characteristics during CO2 adsorption. Using detailed understanding of the physical processes involved in coal multiphysics, a constitutive model based on the continuum mechanics and non-equilibrium thermodynamics is developed in the second part of the dissertation. The model considers changes in gas content in the tight coal matrix through sorption and diffusion processes along with gas leakage from the matrix into fractures where Darcy type flow takes place. Also, the time dependency of coupling processes is accounted for especially the volumetric strains induced by gas sorption and their overall effects on changes in the fracture aperture, hence in the bulk flow conductivity. The novelty of the proposed model specially lies in the derivation of the thermodynamically consistent formulation of time-dependent effective stress law. The next part of the dissertation seeks to validate the developed effective stress law experimentally and to investigate i) the performance of commonly used theoretical models for swelling stress estimation, ii) the validity of thermodynamics coupling coefficient defining the swelling stress and iii) the effect of external stress on coal volumetric strain response. Especially designed experiments on two coal samples are conducted, including time-dependent diffusion and volumetric strain experiments under various stress and CO2 pressure conditions. A new experimental method is proposed to characterize the key input of the model which is the swelling coupling coefficient. Results of these series of experiments also show that the stress induced compression has minor effect on gas desorption. Since the wave velocity and porosity are interrelated, in the last part of the dissertation, some key parameters involved in the set of developed hydromechanical relationships are measured/modelled using acoustic measurement and finite element simulation. First, the effective stress coefficient is predicted using the percolation theory and hydromechanical and ultrasonic laboratory measurements on coal samples. The swelling coefficient representing adsorption induced volumetric strain development is next studied using acoustic simulation. As a newly proposed coupling coefficient in the model development, the relationship between the coefficient and wave velocity are correlated in three pore pressure conditions and its response to each condition is collected and analysed. Finally, the fracture permeability in coal seams is estimated using a novel physics-informed neural network (PINN) technique. In the training of PINN model, a synthetic dataset is built from several ultrasonic measurements and numerical simulations, with input variables of wave velocity and density. This model is successfully applied in a field case study where downhole geophysical logging data is available. In general, the acoustic-driven technique provides a strong and useful pathway to predict model parameters using geophysical logging data in a field setting, where sonic logs are available