Decentralized fault-tolerant control of inland navigation networks: a challenge

Inland waterways are large-scale networks used principally for navigation. Even if the transport planning is an important issue, the water resource management is a crucial point. Indeed, navigation is not possible when there is too little or too much water inside the waterways. Hence, the water resource management of waterways has to be particularly efficient in a context of climate change and increase of water demand. This management has to be done by considering different time and space scales and still requires the development of new methodologies and tools in the topics of the Control and Informatics communities. This work addresses the problem of waterways management in terms of modeling, control, diagnosis and fault-tolerant control by focusing in the inland waterways of the north of France. A review of proposed tools and the ongoing research topics are provided in this paper.Peer ReviewedPostprint (published version

Management tools to study and to deal with effects of climate change on inland waterways

Inland navigation transport takes part in the Trans-European network program (TEN-T ), which aims at promoting this mode of transport by creating favorable conditions for the further development of this sector. The NAIADES Action Program comprises numerous actions and measures to boost transport on inland waterways. Among these actions, the infrastructure issue is dealt with. It requires the inventory of the existing infrastructure and the study of the possible effects of the expected climate change. This was one of the objectives of the GEPET-Eau project (2013-2016), which led to the proposal of multi-scale modeling approaches and adaptive and predictive control architectures. The resilience of inland waterways against the increase of navigation demand and the expected extreme drought and flood events was studied by considering deterministic models. The proposed architecture is suitable to consider two scales of space and time to optimize the water resource allocation among the inland networks and to guarantee the navigation conditions by proposing advanced control and fault detection tools. These approaches, which were designed by considering inland waterways in the north of France, are still being improved. Indeed, it is firstly necessary to consider all the uncertainties that are inherent to large-scale and environmental systems. Secondly, the advanced control and fault detection tools require further development to deal with the very complex dynamics that characterize inland waterways. The main objective of this work is to present the current state of the tools that have been developed in order to study and manage the inland waterways in a climate change context. The global framework that allows describing the link between these two management scales will be detailed. The water resource allocation approach can be based on three different techniques: the Constraint Satisfaction Problem (CSP), the quadratic optimization and the Markov Decision Process (MDP). The MDP-based approach will be emphasized due to its suitability to study complex systems with uncertainties, and its main advantages and drawbacks will be discussed and compared to the other techniques. Advanced control and fault detection tools require an in-depth knowledge of the inland waterway dynamics. Characteristics of navigation reaches, i.e. slope, resonance phenomenon, uncontrolled inputs and interconnections, need to be taken into account. A big effort has been made to improve the modeling step of the navigation reaches by considering the IDZ (Integrator Delay Zero) model. The designed tools are based on this accurate model, and they aim at improving the water level control of each reach of the inland waterways and at performing predictive maintenance strategies by detecting, isolating and forecasting faults on sensors and actuators (limnimeters, gates, locks, etc.). The designed management tools will be presented by considering a part of the real inland navigation network in the north of France. Perspectives and future developments will be described. Peer Reviewed Document type: Articl

Active Learning for Reducing Labeling Effort in Text Classification Tasks

Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on AL in a text classification setting and next to none has involved the more recent, state-of-the-art Natural Language Processing (NLP) models. Here, we present an empirical study that compares different uncertainty-based algorithms with BERT$_{base}$ as the used classifier. We evaluate the algorithms on two NLP classification datasets: Stanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore heuristics that aim to solve presupposed problems of uncertainty-based AL; namely, that it is unscalable and that it is prone to selecting outliers. Furthermore, we explore the influence of the query-pool size on the performance of AL. Whereas it was found that the proposed heuristics for AL did not improve performance of AL; our results show that using uncertainty-based AL with BERT$_{base}$ outperforms random sampling of data. This difference in performance can decrease as the query-pool size gets larger.Comment: Accepted as a conference paper at the joint 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2021). This camera-ready version submitted to BNAIC/BENELEARN, adds several improvements including a more thorough discussion of related work plus an extended discussion section. 28 pages including references and appendice

Risk-Informed Sustainable Development in the Rural Tropics

Many people live in rural areas in tropical regions. Rural development is not merely a contribution to the growth of individual countries. It can be a way to reduce poverty and to increase access to water, health care, and education. Sustainable rural development can also help stop deforestation and reduce live-stock, which generate most of the greenhouse gas emissions. However, eorts to achieve a sustainable rural development are often thwarted by oods, drought, heat waves, and hurricanes, which local communities are not very prepared to tackle. Agricultural practices and local planning are still not very risk-informed. These deciencies are particularly acute in tropical regions, where many Least Developed Countries are located and where there is, however, great potential for rural development. This Special Issue contains 22 studies on best practices for risk awareness; on local risk reduction; on several cases of soil depletion, water pollution, and sustainable access to safe water; and on agronomy, earth sciences, ecology, economy, environmental engineering, geomatics, materials science, and spatial and regional planning in 12 tropical countries

Dealing with Large MDPs, case study of waterway networks supervision

International audienceInland waterway networks are likely to go through heavy changes due to a will in increasing the boat traffic and to the effects of climate change. Those changes would lead to a greater need of an automatic and intelligent planning for an adaptive and resilient water management. A representative model is proposed and tested using MDPs with promising results on the water management optimization. The proposed model permits to coordinate multiple entities over multiple time steps in order to avoid a flood in the waterway network. However, the proposed model suffers a lack of scalability and is unable to represent a real case application. The advantages and limitations of several approaches of the literature are discussed according to our case study

NASA Earth Resources Survey Symposium. Volume 1-D: Water resources

Conference papers on water resources and management are summarized. Summaries cover land use, flood control and prediction, watersheds and the effects of snow melt, soil moisture content, and the usefulness of satellite remote sensors in detecting ground and surface water