Reactive evolutionary path planning for autonomous surface vehicles in lake environments.

Autonomous Surface Vehicles (ASVs) have found a lot of promising applications in aquatic environments, i.e., sea, lakes, rivers, etc. They can be used for applications of paramount importance, such as environmental monitoring of water resources, and for bathymetry to study the characteristics of the basing of a lake/sea or for surveillance in patrol missions, among others. These vehicles can be built with smaller dimensions when compared to regular ships since they do not need an on-board crew for operation. However, they do require at least a telemetry control as well as certain intelligence for making decisions and responding to changing scenarios. Water resources are very important in Paraguay since they provide fresh water for its inhabitants and they are crucial for the main economic activities such as agriculture and cattle raising. Furthermore, they are natural borders with the surrounding countries, and consequently the main transportation route for importing/exporting products. In fact, Paraguay is the third country in the world with the largest fleet of barges after USA and China. Thus, maintaining and monitoring the environmental conditions of these resources is key in the development of the country. This work is focused on the maintenance and monitoring of the greatest lake of the country called Ypacarai Lake. In recent years, the quality of its water has been seriously degraded due to the pollution caused by the low control of the dumping of waste thrown into the Lake. Since it is also a national icon, the government of Paraguay has put a lot of effort in recovering water quality of the Lake. As a result, it is monitored periodically but using manual procedures. Therefore, the primary objective of this work is to develop these monitoring tasks autonomously by means of an ASV with a suitable path planning strategy. Path planning is an active research area in robotics. A particular case is the Coverage Path Planning (CPP) problem, where an algorithm should find a path that achieves the best coverage of the target region to be monitored. This work mainly studies the global CPP, which returns a suitable path considering the initial conditions of the environment where the vehicle moves. The first contribution of this thesis is the modeling of the CPP using Hamiltonian Circuits (HCs) and Eulerian Circuits (ECs). Therefore, a graph adapted to the Ypacarai Lake is created by using a network of wireless beacons located at the shore of the lake, so that they can be used as data exchange points between a control center and the ASV, and also as waypoints. Regarding the proposed modeling, HCs and ECs are paths that begin and end at the same point. Therefore, the ASV travels across a given graph that is defined by a set of wireless beacons. The main difference between HC and EC is that a HC is a tour that visits each vertex only once while EC visits each edge only once. Finding optimal HCs or ECs that minimize the total distance traveled by the ASV are very complex problems known as NP-complete. To solve such problems, a meta-heuristic algorithm can be a suitable approach since they provide quasi-optimal solutions in a reasonable time. In this work, a GA (Genetic Algorithm) approach is proposed and tested. First, an evaluation of the performance of the algorithm with different values of its hyper-parameters has been carried out. Second, the proposed approach has been compared to other approaches such as randomized and greedy algorithms. Third, a thorough comparison between the performance of HC and EC based approaches is presented. The simulation results show that EC-based approach outperforms the HC-based approach almost 2% which in terms of the Lake size is about 1.4 km2 or 140 ha (hectares). Therefore, it has been demonstrated that the modeling of the problem as an Eulerian graph provides better results. Furthermore, it has been investigated the relationship between the number of beacons to be visited and the distance traveled by the ASV in the EC-based approach. Findings indicate that there is a quasi-lineal relationship between the number of beacons and the distance traveled. The second contribution of this work is the development of an on-line learning strategy using the same model but considering dynamic contamination events in the Lake. Dynamic events mean the appearance and evolution of an algae bloom, which is a strong indicator of the degradation of the lake. The strategy is divided into two-phases, the initial exploration phase to discover the presence of the algae bloom and next the intensification phase to focus on the region where the contamination event is detected. This intensification effect is achieved by modifying the beacon-based graph, reducing the number of vertices and selecting those that are closer to the region of interest. The simulation results reveal that the proposed strategy detects two events and monitors them, keeping a high level of coverage while minimizing the distance traveled by the ASV. The proposed scheme is a reactive path planning that adapts to the environmental conditions. This scheme makes decisions in an autonomous way and it switches from the exploratory phase to the intensification phase depending on the external conditions, leading to a variable granularity in the monitoring task. Therefore, there is a balance between coverage and the energy consumed by the ASV. The main benefits obtained from the second contribution includes a better monitoring in the quality of water and control of waste dumping, and the possibility to predict the appearance of algae-bloom from the collected environmental data

Forecasting Harmful Algal Blooms for Western Lake Erie Using Data Driven Machine Learning Techniques

Harmful algal blooms (HAB) have been documented for more than a century occurring all over the world. The western Lake Erie has suffered from Cyanobacteria blooms for many decades. There are currently two widely available HAB forecasting models for Lake Erie. The first forecasting model gives yearly peak bloom forecast while the second provides weekly short-term forecasting and offers size as well as location. This study focuses on bridging the gap of these two models and improve HAB forecast accuracy in western Lake Erie by letting historical observations tell the behavior of HABs. This study tests two machine learning techniques, artificial neural network (ANN) and classification and regression tree (CART), to forecast monthly HAB indicators in western Lake Erie for July to October. ANN and CART models were created with two methods of selecting input variables and two training periods: 2002 to 2011 and 2002 to 2013. First a nutrient loading period method which considers all nutrient contributing variables averaged from March to June and second a Spearman rank correlation to choose separate input sets for each month considering 224 different combinations of averaging and lag periods. The ANN models showed a correlation coefficient increase from 0.70 to 0.77 for the loading method and 0.79 to 0.83 for the Spearman method when extending the training period. The CART models followed a similar trend increasing overall precision from 85.5% to 92.9% for the loading method and 82.1% to 91% for the Spearman method. Both selection methods had similar variable importance with river discharge and phosphorus mass showing high importance across all methods. The major limitation for ANN is the time required for each forecast to be complete while the CART forecasts earlier is only able to produce a class forecast. In future work, the ANN model accuracy can be improved and use new sets of variables to allow earlier HAB forecasts. The final form of ANN and CART models will be coded in a user interface system to forecast HABs. The monthly forecasting system developed allows watershed planners and decision-makers to timely manage HABs in western Lake Erie

Deep learning for the early detection of harmful algal blooms and improving water quality monitoring

Climate change will affect how water sources are managed and monitored. The frequency of algal blooms will increase with climate change as it presents favourable conditions for the reproduction of phytoplankton. During monitoring, possible sensory failures in monitoring systems result in partially filled data which may affect critical systems. Therefore, imputation becomes necessary to decrease error and increase data quality. This work investigates two issues in water quality data analysis: improving data quality and anomaly detection. It consists of three main topics: data imputation, early algal bloom detection using in-situ data and early algal bloom detection using multiple modalities.The data imputation problem is addressed by experimenting with various methods with a water quality dataset that includes four locations around the North Sea and the Irish Sea with different characteristics and high miss rates, testing model generalisability. A novel neural network architecture with self-attention is proposed in which imputation is done in a single pass, reducing execution time. The self-attention components increase the interpretability of the imputation process at each stage of the network, providing knowledge to domain experts.After data curation, algal activity is predicted using transformer networks, between 1 to 7 days ahead, and the importance of the input with regard to the output of the prediction model is explained using SHAP, aiming to explain model behaviour to domain experts which is overlooked in previous approaches. The prediction model improves bloom detection performance by 5% on average and the explanation summarizes the complex structure of the model to input-output relationships. Performance improvements on the initial unimodal bloom detection model are made by incorporating multiple modalities into the detection process which were only used for validation purposes previously. The problem of missing data is also tackled by using coordinated representations, replacing low quality in-situ data with satellite data and vice versa, instead of imputation which may result in biased results

A Review of Harmful Algal Bloom Prediction Models for Lakes and Reservoirs

Anthropogenic activity has led to eutrophication in water bodies across the world. This eutrophication promotes blooms, cyanobacteria being among the most notorious bloom organisms. Cyanobacterial blooms (more commonly referred to as harmful algal blooms (HABs)) can devastate an ecosystem. Cyanobacteria are resilient microorganisms that have adapted to survive under a variety of conditions, often outcompeting other phytoplankton. Some species of cyanobacteria produce toxins that ward off predators. These toxins can negatively affect the health of the aquatic life, but also can impact animals and humans that drink or come in contact with these noxious waters. Although cyanotoxin’s effects on humans are not as well researched as the growth, behavior, and ecological niche of cyanobacteria, their health impacts are of large concern. It is important that research to mitigate and understand cyanobacterial blooms and cyanotoxin production continues. This project supports continued research by addressing an approach to collect and summarize published articles that focus on techniques and models to predict cyanobacterial blooms with the goal of understanding what research has been done to promote future work. The following report summarizes 34 articles from 2003 to 2020 that each describe a mechanistic or data driven model developed to predict the occurrence of cyanobacterial blooms or the presence of cyanotoxins in lakes or reservoirs with similar climates to Utah. These articles showed a shift from more mechanistic approaches to more data driven approaches with time. This resulted in a more individualistic approach to modeling, meaning that models are often produced for a single lake or reservoir and are not easily comparable to other models for different systems

Performance Comparison of the Cogent Confabulation Classifier with Other Commonly Used Supervised Machine Learning Algorithms for Bathing Water Quality Assessment

The purpose of this study was to implement a reliable model for bathing water quality prediction using the Cogent Confabulation classifier and to compare it with other well-known classifiers. This study is a continuation of a previously published work and focuses on the areas of Kaštela Bay and the Brač Channel, located in the Republic of Croatia. The Cogent Confabulation classifier is a thorough and simple method for data classification based on the cogency measure for observed classes. To implement the model, we used data sets constructed of remote sensing data (band values) and in situ measurements presenting ground-truth bathing water quality. Satellite data was retrieved from the Sentinel-3 OLCI satellite and it was atmospherically corrected based on the characteristics and specifications of band wavelengths. The results showed that the Random Forest, K-Nearest Neighbour, and Decision Tree classifiers outperformed the Cogent Confabulation classifier. However, results showed that the Cogent Confabulation classifier achieved better results compared to classifiers based on Bayesian theory. Additionally, a qualitative analysis of the four best classifiers was conducted using spatial maps created in the QGIS tool

Intelligent Orchard Monitoring: An IoT-Based Approach for Real-Time Apple Disease Detection Using Environmental Factors

This research introduces a novel methodology for apple disease detection based on environmental factors, integrating the capabilities of the Internet of Things (IoT). By deploying advanced sensors in orchards, the aim is to facilitate real-time monitoring and transform these spaces into intelligent ecosystems. The methodology encompasses data collection from environmental variables like temperature, humidity, pressure, and light. Using the Mamdani fuzzy inference system (MFIS), the collected data is then employed to predict potential apple diseases. Initial tests conducted in an apple orchard in Shimla, India, demonstrated the system's effectiveness and efficiency, with minimal delays during various phases of the process. The study also offers a comparative analysis with existing state-of-the-art methodologies in the realm of disease detection

Multi-influence factor prediction for water bloom based on multi-sensor system

This paper proposes a new multi-influence factors prediction method for water bloom prediction based on a remote monitor system and multi-sensor data taking into account the integrated effect of multiple influential factors along with the periodicity and random effect of environmental variables. Valid and accurate water-bloom prediction can be obtained by combining various multidimensional time series methods. Comparing the proposed model based on multi-sensors data to a traditional one-dimensional time series model based on one-sensor data, it has been found that a multidimensional model is a useful and accurate model for establishing multiple influential factors time series of water bloom. The optimum model can be used not only to predict water bloom but also to determine the period and random change rule of multiple influential factors

Monitoring, Modelling and Management of Water Quality

Different types of pressures, such as nutrients, micropollutants, microbes, nanoparticles, microplastics, or antibiotic-resistant genes, endanger the quality of water bodies. Evidence-based pollution control needs to be built on the three basic elements of water governance: Monitoring, modeling, and management. Monitoring sets the empirical basis by providing space- and time-dependent information on substance concentrations and loads, as well as driving boundary conditions for assessing water quality trends, water quality statuses, and providing necessary information for the calibration and validation of models. Modeling needs proper system understanding and helps to derive information for times and locations where no monitoring is done or possible. Possible applications are risk assessments for exceedance of quality standards, assessment of regionalized relevance of sources and pathways of pollution, effectiveness of measures, bundles of measures or policies, and assessment of future developments as scenarios or forecasts. Management relies on this information and translates it in a socioeconomic context into specific plans for implementation. Evaluation of success of management plans again includes well-defined monitoring strategies. This book provides an important overview in this context