Hydrolink 2020/4. Artificial intelligent

Topic: Artificial Intelligenc

Science-based restoration monitoring of coastal habitats, Volume Two: Tools for monitoring coastal habitats

Healthy coastal habitats are not only important ecologically; they also support healthy coastal communities and improve the quality of people’s lives. Despite their many benefits and values, coastal habitats have been systematically modified, degraded, and destroyed throughout the United States and its protectorates beginning with European colonization in the 1600’s (Dahl 1990). As a result, many coastal habitats around the United States are in desperate need of restoration. The monitoring of restoration projects, the focus of this document, is necessary to ensure that restoration efforts are successful, to further the science, and to increase the efficiency of future restoration efforts

Valuing the environment in developing countries: Modeling the impact of distrust in public authorities' ability to deliver public services on the citizens' willingness to pay for improved environmental quality

In this paper, we employ the choice experiment method to estimate local citizens' valuation of a public intervention that proposes to improve the quality of an important environmental resource, namely, the Ganges River in India. To elicit citizens' willingness to pay (WTP) higher municipality taxes for an intervention that proposes to improve the quantity and quality of wastewater treated by the local sewage treatment plant (STP), 150 randomly selected citizens of the municipality of Chandernagore, located on the banks of the Ganges River in West Bengal, were interviewed. The findings reveal that almost all (98 percent) of the citizens value the quality of the water and the environment in the Ganges, though a great majority (90 percent) protested the intervention by not choosing the improved STP scenario in at least one of the eight hypothetical markets in which they were asked to participate. When asked their reasons for not preferring the improved scenarios, 92 percent of them stated that they do not trust the authorities to efficiently and effectively manage the funds generated through additional taxes. The protest responses were controlled for with the use of the nested logit model (NLM). The results reveal that the citizens are willing to pay significant amounts to ensure that the intervention takes place and that an improved STP treats larger amounts of wastewater to a higher quality before discharging it to the Ganges. Therefore, to improve the wastewater management services and the related environmental quality in the water bodies into which treated wastewater is deposited, the municipalities could rely—at least to some extent—on their citizens' WTP higher taxes for provision of improved services. To capture this WTP, however, municipalities' performance, trustworthiness, and accountability, as well as the citizens' perceptions of these, should be improved.choice experiment method, nested logit model, willingness to pay, sewage treatment plant, distrust in public authorities,

Monitoring and detecting faults in wastewater treatment plants using deep learning

Wastewater treatment plants use many sensors to control energy consumption and discharge quality. These sensors produce a vast amount of data which can be efficiently monitored by automatic systems. Consequently, several different statistical and learning methods are proposed in the literature which can automatically detect faults. While these methods have shown promising results, the nonlinear dynamics and complex interactions of the variables in wastewater data necessitate more powerful methods with higher learning capacities. In response, this study focusses on modelling faults in the oxidation and nitrification process. Specifically, this study investigates a method based on deep neural networks (specifically, long short-term memory) compared with statistical and traditional machine-learning methods. The network is specifically designed to capture temporal behaviour of sensor data. The proposed method is evaluated on a real-life dataset containing over 5.1 million sensor data points. The method achieved a fault detection rate (recall) of over 92%, thus outperforming traditional methods and enabling timely detection of collective faults

Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data

Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data

Hydro-Ecological Modeling

Water is not only an interesting object to be studied on its own, it also is an important component driving almost all ecological processes occurring in our landscapes. Plant growth depends on soil water content, as well is nutrient turnover by microbes. Water shapes the environment by erosion and sedimentation. Species occur or are lost depending on hydrological conditions, and many infectious diseases are water-borne. Modeling the complex interactions of water and ecosystem processes requires the prediction of hydrological fluxes and stages on the one side and the coupling of the ecosystem process model on the other. While much effort has been given to the development of the hydrological model theory in recent decades, we have just begun to explore the difficulties that occur when coupled model applications are being set up

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Development of Artificial Intelligence Approach to Nowcasting and Forecasting Oyster Norovirus Outbreaks along the U.S. Gulf Coast

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide. This study presents an Artificial Intelligence (AI)-based approach to identifying the primary cause of oyster norovirus outbreaks, nowcasting and forecasting the growing risk of oyster norovirus outbreaks in coastal waters. AI models were developed using Artificial Neural Networks (ANNs) and Genetic Programming (GP) methods and time series of epidemiological and environmental data. Input variable selection techniques, including Random Forests (RF) and Forwards Binary Logistic Regression (FBLR), were used to identify the significant model input variables among six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall and various combinations of the variables with different time lags. In terms of nowcasting, a risk-based GP model was developed to nowcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast, showing the true positive and negative rates of 78.53% and 88.82%, respectively. In terms of forecasting, an ANN model, called ANN-2Day, was presented. The forecasting model was capable of reproducing all historical oyster norovirus outbreaks with the true positive and negative rates of 100.00% and 99.84%, respectively. The sensitivity analysis results of the ANN-2Day model further indicated that oyster norovirus outbreaks were generally linked to the extreme combination of antecedent environmental conditions characterized by low water temperature, low solar radiation, low gage height, low salinity, strong wind, and heavy precipitation. In addition to the GP and ANN-2Day models, a remote sensing–based model was constructed using MODIS Aqua level 2 products. The remote sensing-based model enabled oyster management authorities to expand the prediction of norovirus outbreak risks from areas where monitoring data were accessible to other oyster harvest areas where monitoring stations are not available. In conclusion, the developed AI models enables public health agencies and oyster harvesters to better plan for management interventions and thus makes it possible to achieve a paradigm shift of their daily management and operation from primarily reacting to epidemic incidents of norovirus infection after they have occurred to eliminating (or at least reducing) the risk of costly incidents