Water Level Information Research Articles

Design and Development of a Prototype Water Level Control System for Early Flood Detection Based on The Internet of Things: a Case Study of Kambaniru Dam

Flooding can occur due to overflowing water during the rainy season, therefore early detection of the water level in dams as water runoff processing structures is needed. When heavy rain occurs it can result in flood disasters that are detrimental to communities, dam infrastructure and agricultural land. This research aims to monitor water levels online as initial information about impending flood disasters. Monitoring using Internet of Things (IoT) based technology is intended to obtain water level information in real time. In this device the HC-SR04 ultrasonic sensor is used as a water level reader, a waterflow sensor as a water discharge reader and a servo motor as a sluice gate controller. The results of this research are a prototype of a water level detection device that can provide water level information and can control sluice gates. automatically and can be controlled manually or remotely via the latest notifications in the Blynk application using the MQTT protocol. This research was carried out by taking information based on the results of direct observations and interviews with officers at Kambaniru Dam, Lambanapu Village, Kambera District. In this way, the prototype of this detection tool will be easy to use as initial information about the possibility of flooding.

Assessment of water levels from 43 years of NOAA’s Coastal Ocean Reanalysis (CORA) for the Gulf of Mexico and East Coasts

Coastal water level information is crucial for understanding flood occurrences and changing risks. Here, we validate the preliminary version (0.9) of NOAA’s Coastal Ocean Reanalysis (CORA), which is a 43-year reanalysis (1979–2021) of hourly coastal water levels for the Gulf of Mexico and Atlantic Ocean (i.e., the Gulf and East Coast region, or GEC). CORA-GEC v0.9 was conducted by the Renaissance Computing Institute using the coupled ADCIRC+SWAN coastal circulation and wave model. The model uses an unstructured mesh of nodes with varying spatial resolution that averages 400 m near the coast and is much coarser in the open ocean. Water level variations associated with tides and meteorological forcing are explicitly modeled, while lower-frequency water level variations are included by dynamically assimilating observations from NOAA’s National Water Level Observation Network. We compare CORA to water level observations that were either assimilated or not, and find that the reanalysis generally performs better than a state-of-the-art global ocean reanalysis (GLORYS12) in capturing the variability on monthly, seasonal, and interannual timescales as well as the long-term trend. The variability of hourly non-tidal residuals is also shown to be well resolved in CORA when compared to water level observations. Lastly, we present a case study of extreme water levels and coastal inundations around Miami, Florida to demonstrate an application of CORA for studying flood risks. Our assessment suggests that NOAA’s CORA-GEC v0.9 provides valuable information on water levels and flooding occurrence from 1979–2021 in areas that are experiencing changes across multiple time scales. CORA potentially can enhance flood risk assessment along parts of the U.S. Coast that do not have historical water level observations.

Water Level Inversion Detection Method for Water Level Images without a Scale in Complex Environments

Accurately perceiving changes in water level information is key to achieving the fine control of water and flooding; however, the existing technology cannot achieve water level recognition in complex and harsh environments, such as at night; in haze, rain, or snow; or during obscuration by floating objects or shadows. Therefore, on the basis of a deep analysis of the characteristics of water level images in complex and harsh environments, in this study, we took full advantage of a deep learning network’s ability to characterise semantic features and carried out exploratory research on water level detection in no-water-ruler scenarios based on the two technical means of target detection and semantic segmentation. The related experiments illustrate that all the methods proposed in this study can effectively adapt to complex and harsh environments. The results of this study are valuable for applications in solving the difficulties of accurate water level detection and flood disaster early warnings in poor-visibility scenarios.

Hydrological time series prediction based on IWOA-ALSTM

The prediction of hydrological time series is of great significance for developing flood and drought prevention approaches and is an important component in research on smart water resources. The nonlinear characteristics of hydrological time series are important factors affecting the accuracy of predictions. To enhance the prediction of the nonlinear component in hydrological time series, we employed an improved whale optimisation algorithm (IWOA) to optimise an attention-based long short-term memory (ALSTM) network. The proposed model is termed IWOA-ALSTM. Specifically, we introduced an attention mechanism between two LSTM layers, enabling adaptive focus on distinct features within each time unit to gather information pertaining to a hydrological time series. Furthermore, given the critical impact of the model hyperparameter configuration on the prediction accuracy and operational efficiency, the proposed improved whale optimisation algorithm facilitates the discovery of optimal hyperparameters for the ALSTM model. In this work, we used nonlinear water level information obtained from Hankou station as experimental data. The results of this model were compared with those of genetic algorithms, particle swarm optimisation algorithms and whale optimisation algorithms. The experiments were conducted using five evaluation metrics, namely, the RMSE, MAE, NSE, SI and DR. The results show that the IWOA is effective at optimising the ALSTM and significantly improves the prediction accuracy of nonlinear hydrological time series.

IoT Based Early Flood Detection System with Arduino and Ultrasonic Sensors in Flood-Prone Areas

IoT is one of the focuses of application development carried out by various developers today. The aim is to enable various devices and work independently to meet the various needs of their users. The flood early warning system is one of the much-needed IoT-based applications, enabling users to quickly obtain water level information in an area. This application can help people to be more aware of flood disasters, especially during the rainy season. This research develops a flood early warning system application by utilizing Arduino and ultrasonic sensors installed in flood-prone areas. The sensor is used to measure the water level at a time based on the distance from the water surface to the sensor. When the distance between the water surface and the sensor is less than or equal to the set threshold, the sensor will send data and alerts to the user via email. This research applies the IoT design and development method. In addition, this research also used the C and Python programming language for application prototypes and the MySQL database to store the data. the application in this study was tested using the blackbox method and the results showed that all application functions could run properly.

Monitoring Lake Level Changes in Bosten Lake from 2003-2021 based on Multi-source Satellite Altimetry Data

The investigation of freshwater lake water resource changes in arid regions is crucial for understanding the evolution of regional lake dynamics under climate change, given their value and limited availability. This study aims to overcome the limitations of traditional water level monitoring methods and address the practical need for monitoring water level changes in Lake Bosten, located in the arid and semi-arid regions of China. To this end, we employed ICESat, ICESat-2, and CryoSat-2 altimetry data, as well as Landsat 8-OLI optical remote sensing data, to obtain the water level information for Bosten Lake from 2003 to 2022. We analyzed the temporal variation of the lake level and discussed the response of the lake level to climate and human activities in the context of temperature, precipitation data, and human water use. The water level of Lake Bosten exhibited a decreasing and then increasing trend, with three rising and three falling intervals, respectively. The intra-year variation of the lake water level exhibited bimodal characteristics, and the fluctuation range of water level during abundant water periods was greater than that during dry periods. Finally, we correlated the extracted lake water level with the measured water level at the hydrological station and found a significant positive linear correlation between the observed water level of Bosten Lake based on satellite altimetry and the measured water level at the hydrological station, with an overall correlation coefficient of 0.896 and 0.934 (P < 0.01), indicating high accuracy of the water level results obtained in this study. The dynamics of Lake Bosten are influenced by a combination of climate change, hydrological elements, and human activities, with a relatively weak relationship between climate change and lake volume and a strong influence of human activities. Multi-source altimetry satellites provide a powerful tool for monitoring lake water levels over a long time scale, which is critical for studying lake water level changes and their response to climate and the environment.

Exploring the Effect of Meteorological Factors on Predicting Hourly Water Levels Based on CEEMDAN and LSTM

The magnitude of tidal energy depends on changes in ocean water levels, and by accurately predicting water level changes, tidal power plants can be effectively helped to plan and optimize the timing of power generation to maximize energy harvesting efficiency. The time-dependent nature of water level changes results in water level data being of the time-series type and is essential for both short- and long-term forecasting. Real-time water level information is essential for studying tidal power, and the National Oceanic and Atmospheric Administration (NOAA) has real-time water level information, making the NOAA data useful for such studies. In this paper, long short-term memory (LSTM) and its variants, stack long short-term memory (StackLSTM) and bi-directional long short-term memory (BiLSTM), are used to predict water levels at three sites and compared with classical machine learning algorithms, e.g., support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). This study aims to investigate the effects of wind speed (WS), wind direction (WD), gusts (WG), air temperature (AT), and atmospheric pressure (Baro) on predicting hourly water levels (WL). The results show that the highest coefficient of determination (R2) was obtained at all meteorological factors when used as inputs, except at the La Jolla site. (Burlington station (R2) = 0.721, Kahului station (R2) = 0.852). In the final part of this article, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm was introduced into various models, and the results showed a significant improvement in predicting water levels at each site. Among them, the CEEMDAN-BiLSTM algorithm performed the best, with an average RMSE of 0.0759 mh−1 for the prediction of three sites. This indicates that applying the CEEMDAN algorithm to deep learning has a more stable predictive performance for water level forecasting in different regions.

Predictive control for the operation of cascade pumping stations in water supply canal systems considering energy consumption and costs

Water supply canal systems (WSCSs) have a significant environmental and energetic impact due to the large amount of energy consumed in water pumping and water losses. The safe and efficient operation of these systems is crucial. This study presents a closed-loop predictive control method for the operation of pumping stations in WSCSs, considering operation safety and energy consumption. In the proposed method, a linear prediction model derived from the hydrodynamic model is used for state prediction; data assimilation is used to correct the prediction model based on the feedback water level information; on the basis of the prediction results, the optimization control model for energy or cost saving is rolling established and solved to obtain the optimal pump control strategy. The method was tested on a virtual simulation model of a WSCS with two pumping stations under uncertain disturbances and compared to an open-loop optimal control method. The results demonstrated that the proposed method's cost- and energy-driven optimization strategies could reduce the system's operating energy and cost by 5.1 % and 5.5 %, respectively while ensuring the safe operation of the system. Compared with the result of 1.4 % energy reduction and 0.2 % cost reduction under the open-loop optimal control, the proposed method can retain a better optimization effect under uncertain disturbance and has good robustness. Furthermore, the method can be modified according to the needs of the WSCSs for pump control.

Locating real-time water level sensors in coastal communities to assess flood risk by optimizing across multiple objectives

Coastal communities around the world are experiencing increased flooding. Water level sensors provide real-time information on water levels and detections of flood risk. Previous sensor installations, however, have relied on qualitative judgments or limited quantitative factors to decide on sensor locations. Here, we provide a method to optimally place real-time water level sensors across a community. We utilize a multi-objective optimization approach, including traditional measures of sensor network performance such as coverage and uncertainty, and new flood-specific parameters such as hazard estimations (flood likelihood, critical infrastructure exposure), serviceability (sensor accessibility), and social vulnerability (socio-economic index, vulnerable residential communities index). We propose a workflow combining quantitative analyses with local expertise and experience. We show the method is able to reduce the set of possible new sensor locations to just 1.3% of the full solution set, supporting effective and feasible community decision-making. The method also supports sequential expansion of a sensor network, creating a network that provides detailed and accurate real-time water level information at the hyperlocal level for flood risk assessment and mitigation in coastal communities.

A Spatial-Reduction Attention-Based BiGRU Network for Water Level Prediction

According to the statistics of ship traffic accidents on inland waterways, potential safety hazards such as stranding, hitting rocks, and suspending navigation are on the increase because of the sudden rise and fall of the water level, which may result in fatalities, environmental devastation, and massive economic losses. In view of this situation, the purpose of this paper is to propose a high-accuracy water-level-prediction model based on the combination of the spatial-reduction attention and bidirectional gate recurrent unit (SRA-BiGRU), which provides support for ensuring the safe navigation of ships, guiding the reasonable stowage of ships, and flood prevention. The first contribution of this model is that it makes use of its strong fitting ability to capture nonlinear characteristics, and it fully considers the time series of water-level data. Secondly, the bidirectional recurrent neural network structure makes full use of past and future water-level information in the mapping process between input and output sequences. Thirdly, and most importantly, the introduction of spatial-reduction attention on the basis of BiGRU can not only automatically capture the correlations between the hidden vectors generated by BiGRU to address the issue of precision degradation due to the extended time span in water-level-forecasting tasks but can also make full use of the spatial information between water-level stations by emphasizing the influence of significant features on the prediction results. It is noteworthy that comparative experiments gradually prove the superiority of GRU, bidirectional recurrent neural network structure, and spatial-reduction attention, demonstrating that SRA-BiGRU is a water-level-prediction model with high availability, high accuracy, and high robustness.

Storm Kyrill and the storms of mid-January 2007: Societal and Energy Impacts in Europe

Abstract. January 2007 was a stormy period in Europe with impacts on societal infrastructure and implications for energy meteorology. A series of cyclones tracked across the North Atlantic and into Europe during the two week period 8–22 January 2007. For many parts of Europe, Storm Kyrill on 18 January 2007 was the most important of these for the infrastructure damage that it caused. It had the highest European storm-related insurance losses in recent history. The storm spawned a high intensity derecho that started in Germany and travelled across eastern Europe into the Black Sea region. It was associated with severe convection, lightning, several tornadoes, and strong wind gusts. The storm caused over 50 fatalities, widespread disruption of transport and power networks, and a lot of forest damage. The highest coastal water levels for the month at many tide gauge stations in northwest Europe (and also for the year, in some cases) were registered during Storm Kyrill. This contribution presents a literature review of the storm characteristics and its impacts. This is followed by an analysis of the North Sea tide gauge data to assess the storm surge, tidal variation, and short-period seiche component around the North Sea. The water level information is compared with shipping accidents and offshore incidents to assess possible links. Unusually large waves had been registered at the FINO1 offshore wind energy research platform and off the northern coast of the Netherlands only a couple of months previously on 1 November 2006. While Storm Kyrill caused a lot of societal damage on land areas, there was comparatively little coastal damage around the North Sea and few reports of offshore infrastructure damage linked to wave strikes.

Calibrated river-level estimation from river cameras using convolutional neural networks

Abstract Monitoring river water levels is essential for the study of floods and mitigating their risks. River gauges are a well-established method for river water-level monitoring but many flood-prone areas are ungauged and must be studied through gauges located several kilometers away. Taking advantage of river cameras to observe river water levels is an accessible and flexible solution but it requires automation. However, current automated methods are only able to extract uncalibrated river water-level indexes from the images, meaning that these indexes are relative to the field of view of the camera, which limits their application. With this work, we propose a new approach to automatically estimate calibrated river water-level indexes from images of rivers. This approach is based on the creation of a new dataset of 32,715 images coming from 95 river cameras in the UK and Ireland, cross-referenced with gauge data (river water-level information), which allowed us to train convolutional neural networks. These networks are able to accurately produce two types of calibrated river water-level indexes from images: one for continuous river water-level monitoring, and the other for flood event detection. This work is an important step toward the automated use of cameras for flood monitoring.

Estimation of wastewater flowrate in a gravitational sewer line based on a low-cost distance sensor

Abstract Wastewater flowrate exhibits valuable information on the conditions in a sewer line. However, existing hardware flowrate sensors are rather expensive, vulnerable to fouling and breakages, and require frequent and laborious maintenance. Therefore, they are typically only mounted in a few key locations of a sewer system, leading to a lack of important information in a major part of the network. Utilizing more cost-effective sensors and a soft sensor approach is advisable for estimating the flowrate at locations where hardware sensors are lacking. Here, the development and testing of a data-driven soft sensor for estimating the wastewater flowrate based on the water level information obtained from a low-cost ultrasonic distance sensor are presented. The research included a long-term functionality testing period of the sensor in a cold region. The soft sensor-based flowrate was applied to estimate inflow and infiltration, indicating the conditions of the sewer line. The harsh conditions inside the sewer manhole caused challenges for the reliability of the distance measurement based on an ultrasonic principle. With the developed model-based soft sensor, it seems possible to accurately estimate the wastewater flowrate. Together with additional information, it might also enable accurate monitoring of inflows and infiltrations.

Implementasi NodeMCU sebagai Sistem Deteksi Dini Bahaya Banjir di Purwodadi Berbasis Web dengan Notifikasi Bot Telegram

Purwodadi Town is one of the areas of Central Java, every time it rains some places will be flooded. And the heavy and long-lasting rainfall resulted in flooding. Floods cause losses for affected communities and damage existing infrastructure. One solution is to make an early warning system for flood disasters, the researchers designed a device based on the nodemcu microcontroller and used telegram as a notification. The researcher applies the prototype method to analyze all the needs starting from the input, output and interface description, then do the design. The results of this study are an early flood hazard detection system with telegram notifications that can be accessed through a website with various features, namely real-time water level information, graphical water level reports, and additional users to get notifications. The prototype produced from this research, the public can know whether a flood occurs or not so that they can anticipate before the flood occurs

Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things

Natural disasters such as severe flooding can cause catastrophic losses to properties and human lives. Constant real-time water level monitoring prior to a flooding event can minimise damages and casualties. Many of the currently deployed water level monitoring systems typically use a combination of float-type or ultrasonic sensing, image processing and computer vision techniques. However, these systems incur high computing and hardware requirements, which hinder the deployment of such systems in resource-constrained and low-cost environments. The recent development of technologies empowered by the Internet of things (IoT) and edge computing have enabled real-time systems to be deployed at a significantly lower cost and a far more distributed manner. In this paper, we propose an architecture for flood monitoring using RGB-D cameras with stereoscopic capabilities to measure the water level in an open environment. Our system uses image preprocessing techniques to account for chromatic aberration due to overexposure, followed by postprocessing before the depth readings are extracted. Data processing and water level information extraction are entirely performed on an edge computing device, therefore greatly reducing the amount of data transmitted to the cloud server. We practically implemented and experimentally validated this system in the real world, under a wide range of weather and lighting conditions. Our results showed promising outcomes and demonstrated the applicability of our proposed system in a wider context.

Monitoring and Analysis of Water Surface Slope of the Yarlung Zangbo River Based on Remote Sensing

Global warming has led to an increase in interest in river hydrology. Water surface slope is a basic parameter of river morphology and is critical in understanding riverine ecosystems. The Yarlung Zangbo River is an important international river. Scientific understanding of its hydrology has been severely limited by its harsh topography and the sparse distribution of measurement stations. This paper describes the first attempt to use ICESat-2 satellite altimetry data to calculate river water slope and introduces a new technique for studying river hydrology. Water level data for different stretches of the Yarlung Zangbo River were obtained from high spatial resolution ICESat-2 altimetry data. The data were analyzed to determine the water surface slope of the Yarlung Zangbo River, and variation in the water surface slope was examined for several river sections. The water surface slope of the Yarlung Zangbo River was greatest in summer and autumn and least in winter and spring. There may be seasonal cyclic changes in the water surface slope. The range for water surface slope was large and the difference in extremes is significant; the maximum average water surface slope was 1935.17 cm/km and the minimum water surface slope was 0.08 cm/km. The water surface slope of the Yarlung Zangbo River can be characterized as small in the upper and middle reaches and large in the lower reaches; it is related to dry and wet conditions determined by topography and climate. The upper and middle reaches of the Yarlung Zangbo River are semi-arid and the lower reaches of the Yarlung Zangbo River span three climate zones: semi-arid, semi-humid and humid. In addition, the water level information measured by ICESat-2 data is basically similar to the water level information measured by Hydroweb data, except that the water level information of individual virtual stations is quite different due to special reasons. It was found that ICESat-2 data had higher-accuracy water level data results and more river location information. Therefore, ICESat-2 satellite altimetry data can provide a baseline reference for the study of the Yarlung Zangbo River hydrology, and the technique used is applicable to other rivers.

Self‐Powered Intelligent Buoy Based on Triboelectric Nanogenerator for Water Level Alarming

AbstractWith increasing global warming, catastrophic floods have threatened people's lives seriously and caused huge economic losses. However, present water hazard alarming systems generally rely on commercial batteries, limiting the intelligent development of disaster prevention planning and maintenance costs. In order to break the limitation, this work applies triboelectric nanogenerators (TENGs) to water hazard alarming. A spherical TENG device with four spiral units is designed to harvest water wave energy, and charge excitation modules (CEMs) are created and integrated with the TENG to improve its electric output. The output current and output power of the spherical TENG with CEMs can reach 15.09 mA and 24.48 mW, which are increased by 250.5 and 4.0 times, compared to the TENG without CEMs. Based on the charge excitation TENG, a self‐powered intelligent buoy is constructed. Utilizing the buoy to transmit 433 MHz radio frequency signals to 25 meters away, a water level alarm system and a water level information exchange system with a mobile phone are successfully realized. This work extends applications of TENGs toward water wave energy harvesting and provides a new strategy for water hazard alarming, which is conducive to the fields of carbon neutralization, Internet of Things, and disaster prevention.

Evaluating SWOT water level information using a large scale hydrology simulator: A case study over India

An important goal of the Surface Water and Ocean Topography (SWOT) mission is to provide river width, height, slope to estimate global river discharges. To appraise the feasibility of SWOT data over India, the present study utilizes the SWOT simulator from Centre National D’Etudes Spatiales (CNES) to generate a long time-series (three years) of SWOT observations over the Mahanadi River basin in India. Two independent sources of river geometry are used in this study, namely satellite-based and hydrodynamic model-based estimates. Satellite-based river geometry is derived using a web-based User Interface (UI) developed in-house to generate time series of accurate river width shapefiles from Sentinel-1 satellite imagery. To compare these results, a 1D unsteady flow analysis is carried out over the Mahanadi River to simulate channel water depth and inundation extent using the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model. The simulated observations are used in this study to assess the potential error in SWOT channel water depth measurements. Results indicate a good spatial correlation between the water surface area derived from Sentinel and HEC-RAS models with a correlation of 0.74. The simulated data shows a bias of 20 cm compared to gauge observations. This study is conducted as a proof of concept in demonstrating the ability of simulated SWOT data in capturing river water surface elevation with respect to the conventional HEC-RAS model. It also implements a new tool in google earth engine (GEE) to generate input to SWOT simulator.

Evaluation of Water Level in Flowing Channels Using Ultrasonic Sensors

Monitoring flow in channels is difficult, expensive, and potentially dangerous; hence, alternatives minimizing these factors are sought and indirect methods to measure the flow based on water-level information, among others, are employed. Ultrasonic sensors along with Arduino are widely used to monitor levels in reservoirs; however, the accuracy of this method in turbulent flow regimes has not been evaluated. Therefore, in this study, we evaluated the level of open channel flows using a combination of Arduino and ultrasonic sensors whilst considering turbulence. Additionally, we statistically compared the simultaneous measurements of levels obtained using five ultrasonic sensors with those of five rulers filmed individually along an artificial channel, for four permanent and two transient regimes. The results showed that the errors in measurements increased with increasing turbulence. These errors were within the range of hydraulic measurements (<0.020 m), indicating that the procedure is valid for experienced conditions. Therefore, the combination of Arduino and ultrasonic sensors is a technically and economically viable alternative. However, calibrating and validating the sensors for distances greater than 0.400 m should be performed with care because the bench tests performed in static conditions were limitedly accurate in measuring distances greater than 0.200 m.

Measuring Water Levels by Handheld Smartphones – A contribution to exploit crowdsourcing in the spatio temporal densification of water gauging networks

Global climate change leads to an increase in local heavy rainfall events causing nearly unpredictable flash floods worldwide. This paper introduces a novel and flexible low-cost water gauging technology, called Open Water Levels, using smartphones as low-cost measuring devices enabling the crowdsourcing of water levels on demand with accuracies of a few centimetres. This merely requires smartphone camera images of a riverbank and approximate values of the camera position and rotation measured by smartphone sensors. The images are analysed for the water line that is further projected into object space and intersected with a 3D model, e.g. from a GIS database, to derive water level information.