Survey of Landslide Warning Systems and their Applicability in Mauritius

Landslide is major problem in several countries causing loss of lives and major infrastructural damage. Several systems have been set-up for monitoring and predicting landslides in different countries where this problem is prevalent. These systems integrate sensing mechanism with communication systems and GPS to detect landslide conditions and alert concerned parties via sms, emails and other appropriate means. Wireless sensor networks have also been widely deployed for landslide monitoring. Mauritius which is an island nation situated in the Indian Ocean has recently faced several problems due to extreme climatic conditions such as torrential rains and flash floods that have led to major landslide problems in different parts of the island. However, to date, there is no adequate system in place to monitor landslides. This paper surveys the different landslide modelling and warning systems that have been deployed worldwide and assesses their suitability for Mauritius. Given the excellent mobile network coverage available in Mauritius, a landslide warning system based on sms notifications appears to be a viable solution for Mauritius. A potential framework for a landslide monitoring system for Mauritius is therefore proposed along with a feasibility analysis

Two Solutions of Soil Moisture Sensing with RFID for Landslide Monitoring

Two solutions for UHF RFID tags for soil moisture sensing were designed and are described in this paper. In the first, two conventional tags (standard transponders) are employed: one, placed close to the soil surface, is the sensor tag, while the other, separated from the soil, is the reference for system calibration. By transmission power ramps, the tag’s turn-on power levels are measured and correlated with soil condition (dry or wet). In the second solution, the SL900A chip, which supports up to two external sensors and an internal temperature sensor, is used. An interdigital capacitive sensor was connected to the transponder chip and used for soil moisture measurement. In a novel design for an UHF RFID tag the sensor is placed below the soil surface, while the transponder and antenna are above the soil to improve communication. Both solutions are evaluated practically and results show the presence of water in soil can be remotely detected allowing for their application in landslide monitoring

Low Cost and Flexible UAV Deployment of Sensors

This paper presents a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source , is designed for researchers, students and makers for a broad range of exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system is illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperature in a lake

Digitalization and real-time control to mitigate environmental impacts along rivers: Focus on artificial barriers, hydropower systems and European priorities

Hydropower globally represents the main source of renewable energy, and provides several benefits, e.g., water storage and flexibility; on the other hand, it may cause significant impacts on the environment. Hence sustainable hydropower needs to achieve a balance between electricity generation, impacts on ecosystems and benefits on society, supporting the achievement of the Green Deal targets. The implementation of digital, information, communication and control (DICC) technologies is emerging as an effective strategy to support such a trade-off, especially in the European Union (EU), fostering both the green and the digital transitions. In this study, we show how DICC can foster the environmental integration of hydropower into the Earth spheres, with focus on the hydrosphere (e.g., on water quality and quantity, hydropeaking mitigation, environmental flow control), biosphere (e.g., improvement of riparian vegetation, fish habitat and migration), atmosphere (reduction of methane emissions and evaporation from reservoirs), lithosphere (better sediment management, reduction of seepages), and on the anthroposphere (e.g., reduction of pollution associated to combined sewer overflows, chemicals, plastics and microplastics). With reference to the abovementioned Earth spheres, the main DICC applications, case studies, challenges, Technology Readiness Level (TRL), benefits and limitations, and transversal benefits for energy generation and predictive Operation and Maintenance (O&M), are discussed. The priorities for the European Union are highlighted. Although the paper focuses primarly on hydropower, analogous considerations are valid for any artificial barrier, water reservoir and civil structure which interferes with freshwater systems.Digitalization and real-time control to mitigate environmental impacts along rivers: Focus on artificial barriers, hydropower systems and European prioritiespublishedVersio

Advancing geospatial information management for disaster risk management in the Caribbean

The Caribbean is highly vulnerable to the impacts of climate change, extreme weather events and other natural hazards. The subregion is also exposed to anthropogenic hazards, including petroleum and other industrial chemical spills, fires, and soil, air and water pollution. These hazards can result in loss of life and other health impacts, damage to infrastructure, social and economic disruptions and ecological degradation. To significantly reduce the negative effects of these hazards, it is important that key stakeholders, including national disaster management agencies, development partners, and the private sector, particularly insurance companies be integrally engaged in the shaping of comprehensive disaster risk management (DRM) strategies and plans. The success of DRM will depend on the effective management of relevant information and data. Geospatial Information Management (GIM) has enabled more timely, data-driven, informed DRM decision-making. This research provides an introduction to the status and use of GIM in support of DRM in the Caribbean region. The data and information obtained from on-line surveys and desk studies indicated that the Caribbean countries are at varying stages of progress towards the integration of GIM in DRM. Policy setting, legislation, education, capacity building, technological investment and institutional strengthening driving geospatial data management are priority areas identified for further advancing this progress. The study offers recommendations towards further strengthening the use of GIM in DRM both nationally and at the level of the Caribbean region.Abstract .-- Introduction .-- I. Methodology .-- II. Geospatial Information Management and Disaster Risk Management .-- III. Geospatial Information Management for Disaster Risk Management in the Caribbean .-- IV. Recommendations for advancing geospatial information management for disaster risk management

Exploitation of X-band weather radar data in the Andes high mountains and its application in hydrology: a machine learning approach

Rainfall in the tropical Andes high mountains is paramount for understanding complex hydrological and ecological phenomena that take place in this distinctive area of the world. Here, rainfall drives imminent hazards such as severe floods, rainfall-induced landslides, different types of erosion, among others. Nonetheless, sparse and uneven distributed rain gauge networks as well as low- resolution satellite imagery are not sufficient to capture its high variability and complex dynamics in the irregular topography of high mountains at appropriate temporal and spatial scales. This results in both, a lack of knowledge about rainfall patterns, as well as a poor understanding of rainfall microphysics, which to date are largely underexplored in the tropical Andes. Therefore, this investigation focuses on the deployment and exploitation of single-polarization (SP) X-band weather radars in the Andean high mountain regions of southern Ecuador, applicable to quantitative precipitation estimation (QPE) and discharge forecasting. This work leverages radar rainfall data by exploring a machine learning (ML) approach. The main aims of the thesis were: (i) The deployment of a first X-band weather radar network in tropical high mountains, (ii) the physically-based QPE of X-band radar retrievals, (iii) the optimization of radar QPE by using a ML-based model and (iv) a discharge forecasting application using a ML-based model and SP X-band radar data. As a starting point, deployment of the first weather radar network in tropical high mountains was carried out. A complete framework for data transmission was set for communication among the network. The highest radar in the network (4450 m a.s.l.) was selected in this study for exploiting the potential of SP X-band radar data in the Andes. First and foremost, physically-based QPE was performed through the derivation of Z-R relationships. For this, data from three disdrometers at different geographic locations and elevation were used. Several rainfall events were selected in order to perform a classification of rainfall types based on the mean volume diameter (Dm [mm]). Derived Z-R relations confirmed the high variability in their parameters due to different rainfall types in the study area. Afterwards, the optimization of radar QPE was pursued by using a ML approach as an alternative to the common physically-based QPE method by means of the Z-R relation. For this, radar QPE was tackled by using two different approaches. The first one was conducted by implementing a step-wise approach where reflectivity correction is performed in a step-by-step basis (i.e., clutter removal, attenuation correction). Finally a locally derived Z-R relationship was applied for obtaining radar QPE. Rain gauge-bias adjustment was neglected because the availability of rain gauge data at near-real time is limited and infrequent in the study area. The second one was conducted by an implementation of a radar QPE model that used the Random Forest (RF) algorithm and reflectivity derived features as inputs for the model. Finally, the performances of both models were compared against rain gauge data. The results showed that the ML-based model outperformed the step-wise approach, making it possible to obtain radar QPE without the need of rain gauge data after the model was implemented. It also allowed to extend the useful range of the radar image (i.e., up to 50 km). Radar QPE can be generally used as input for discharge forecasting models if available. However, one could expect from ML-based models as RF, the ability to map radar data to the target variable (discharge) without any intermediate step (e.g., transformation from reflectivity to rainfall rate). Thus, a comparison for discharge forecasting was performed between RF models that used different input data type. Input data for the relevant models were obtained either from native reflectivity records (i.e., reflectivity corrected from unrealistic measurements) or derived radar-rainfall data (i.e., radar QPE). Results showed that both models performed alike. This proved the suitability of using native radar data (reflectivity) for discharge forecasting in mountain regions. This could be extrapolated in the advantages of deploying radar networks and use their information directly to fed early-warning systems regardless of the availability of rain gauges at ground. In summary, this investigation (i) participated on the deployment of the first weather radar network in tropical high mountains, (ii) significantly contributed to a deeper understanding of rainfall microphysics and its variability in the high tropical Andes by using disdrometer data and (iii) exploited, for the very first time, the native X-band radar reflectivity as a suitable input for ML-based models for both, optimized radar QPE and discharge forecasting. The latter highlighted the benefits and potentials of using a ML approach in radar hydrology. The research generally accounted for ground monitoring limitations commonly found in mountain regions and provided a promising alternative with leveraging the cost-effective X-band technology in the steep terrain of the Andean Cordillera

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

Earthquake Engineering

The book Earthquake Engineering - From Engineering Seismology to Optimal Seismic Design of Engineering Structures contains fifteen chapters written by researchers and experts in the fields of earthquake and structural engineering. This book provides the state-of-the-art on recent progress in the field of seimology, earthquake engineering and structural engineering. The book should be useful to graduate students, researchers and practicing structural engineers. It deals with seismicity, seismic hazard assessment and system oriented emergency response for abrupt earthquake disaster, the nature and the components of strong ground motions and several other interesting topics, such as dam-induced earthquakes, seismic stability of slopes and landslides. The book also tackles the dynamic response of underground pipes to blast loads, the optimal seismic design of RC multi-storey buildings, the finite-element analysis of cable-stayed bridges under strong ground motions and the acute psychiatric trauma intervention due to earthquakes