[go: up one dir, main page]

WO2022024009A1 - Plate-forme en nuage pour réservoirs d'eaux souterraines - Google Patents

Plate-forme en nuage pour réservoirs d'eaux souterraines Download PDF

Info

Publication number
WO2022024009A1
WO2022024009A1 PCT/IB2021/056884 IB2021056884W WO2022024009A1 WO 2022024009 A1 WO2022024009 A1 WO 2022024009A1 IB 2021056884 W IB2021056884 W IB 2021056884W WO 2022024009 A1 WO2022024009 A1 WO 2022024009A1
Authority
WO
WIPO (PCT)
Prior art keywords
parameters
data
lot
cloud platform
underground water
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2021/056884
Other languages
English (en)
Inventor
Michail MAVROFORAKIS
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Inttrust SA Information Technology Trust
Original Assignee
Inttrust SA Information Technology Trust
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Inttrust SA Information Technology Trust filed Critical Inttrust SA Information Technology Trust
Priority to EP21763405.4A priority Critical patent/EP4189443A1/fr
Publication of WO2022024009A1 publication Critical patent/WO2022024009A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/18—Water
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
    • H04L65/40—Support for services or applications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • E—FIXED CONSTRUCTIONS
    • E03—WATER SUPPLY; SEWERAGE
    • E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
    • E03B11/00—Arrangements or adaptations of tanks for water supply
    • E03B11/10—Arrangements or adaptations of tanks for water supply for public or like main water supply
    • E03B11/14—Arrangements or adaptations of tanks for water supply for public or like main water supply of underground tanks
    • E—FIXED CONSTRUCTIONS
    • E03—WATER SUPPLY; SEWERAGE
    • E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
    • E03B3/00—Methods or installations for obtaining or collecting drinking water or tap water
    • E03B3/06—Methods or installations for obtaining or collecting drinking water or tap water from underground
    • E03B3/08—Obtaining and confining water by means of wells
    • E—FIXED CONSTRUCTIONS
    • E03—WATER SUPPLY; SEWERAGE
    • E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
    • E03B3/00—Methods or installations for obtaining or collecting drinking water or tap water
    • E03B3/06—Methods or installations for obtaining or collecting drinking water or tap water from underground
    • E03B3/08—Obtaining and confining water by means of wells
    • E03B3/16—Component parts of wells
    • E—FIXED CONSTRUCTIONS
    • E03—WATER SUPPLY; SEWERAGE
    • E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
    • E03B5/00—Use of pumping plants or installations; Layouts thereof
    • E03B5/04—Use of pumping plants or installations; Layouts thereof arranged in wells
    • E03B5/06—Special equipment, e.g. well seals and connections for well casings or the like
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use

Definitions

  • the invention refers to a computer-implemented method to monitor and analyze underground waters locally and in a plurality of underground water reservoirs.
  • the invention further refers to an apparatus and Information Technology (IT) tools for the implementation of the method.
  • IT Information Technology
  • Existing methods monitor on an ad-hoc and sparse basis the underground waters level depth of a well at a specific geographic location, as well as some relevant aspects of the quality of a well’s water or the degree of its contamination.
  • existing methods analyze an underground water reservoir independently from other reservoirs, rather than revealing information regarding dependencies on the qualities and behavior of a plurality of reservoirs.
  • the object of the invention is to provide a computer-implemented method for the systematic, continuous, on-line, real-time monitoring of the properties of underground waters.
  • a further object of the invention is a computer-implemented method for modeling the behavior of underground waters under environmental and/or users’ actions in local, regional and global geographic areas.
  • a further object of the invention is a computer- implemented method for the prediction of the behavior of underground waters under usage and environmental scenarios.
  • the invention aims in a Cloud-based ecosystem that is used for improving the sustainability of underground water systems.
  • the invention aims also in an apparatus for the implementation of the computer-implemented inventions.
  • the computer-implemented method of the invention is used for the on-line, real-time monitoring and analysis of the properties of a plurality of underground water reservoirs, each one of the plurality of underground water reservoirs associated with a distinct well.
  • the method comprises:
  • processing unit • processing the data transmitted to the Cloud platform by said processing unit, so as to infer inter-dependencies/correlations between the usage parameters, the water parameters and the environmental parameters of the plurality of underground water reservoirs.
  • Cloud platform used in the present application encompasses generic client-server platforms in www or local.
  • a “plurality” means more than one.
  • Water parameters describe the quantity/capacity and quality of the water in the underground water reservoirs. Examples of such parameters are the Static Water Level (SWL), i.e. the distance from the ground of the surface of underground waters local reservoir, water depth, the quality, temperature, pH, electrical-conductivity, Total Dissolved Solids (TDS), Oxidation- Reduction Potential (ORP), Dissolved Oxygen (DO).
  • SWL Static Water Level
  • TDS Total Dissolved Solids
  • ORP Oxidation- Reduction Potential
  • DO Dissolved Oxygen
  • the water depth measurements in rest i.e. when the installation does not pump, may be tracked and analyzed in long term, typically in weekly or monthly aggregations throughout a year or more, in order to estimate statistics and infer trends that describe the status and capacity of the underground water reservoir in the greater region.
  • Usage parameters are pumping parameters and controlling parameters.
  • Pumping parameters relate to the parameters that describe the pumping of the water from the underground water reservoir. Examples of such parameters are Pumping Water Level (PWL), pumping times and durations, Drawdown (DD), time of water level stabilization, pumping and refill rate, pumped quantity.
  • Controlling parameters relate to loT device health-check, diagnostics and process-optimization parameters.
  • Environmental parameters relate to the environmental conditions. Examples of environmental parameters are atmospheric pressure, temperature, humidity, wind parameters, rain gauge data, solar power and UV radiation, dew point, soil moisture, soil pH.
  • Examples of Cognitive techniques e.g. Machine Learning techniques that may be applied by the processing unit of the Cloud platform, are neural networks, support vector machines (SVMs), decision trees, random forests etc.
  • the processing unit of the Cloud platform may further apply statistical analysis techniques and/or signal analysis techniques.
  • the method may use data analytics via Cognitive techniques, e.g.
  • the Cloud platform may process the data transmitted by the loT devices to define parameters for controlling the loT devices, process optimization and/or pumping parameters of the underground water reservoirs to be employed in the usage of the underground water reservoirs and transmitting said parameters to the loT devices.
  • At least an loT device that is used in a method according to the invention, includes a local memory unit and the method includes storing and processing the data monitored by the loT device to the local memory unit of the respective loT device -“Edge computing”.
  • the data transmitted by the loT device may be raw data or data that have been monitored and further processed - including data generated from the raw data that are monitored - by the loT device.
  • At least an loT device includes a local processing unit and the method includes processing locally the data that is monitored by the loT device by the local processing unit of the respective loT device to derive at least a usage parameter and/or an environmental parameter and/or a water parameter reflecting the status and the behavior of the respective underground water reservoir.
  • at least an loT device includes surveillance means of the well and/or the loT device.
  • the performance and accuracy of the loT device can be re-assessed and re-calibrated periodically, for example every month, when the loT device installation, e.g., maintenance, or environmental factors, e.g. summer/winter, change. Moreover, if the quantity and/or quality of the water tend to drop, relevant warnings are issued and changes to the pumping schedule are proposed.
  • the data monitored and transmitted by the loT devices may include a time series of a parameter.
  • an loT device for monitoring the properties of an underground water reservoir comprises sensors to monitor time series of a parameter of an underground water reservoir and means to transmit and receive data to and from a Cloud platform.
  • the loT device of the invention may include a memory unit and a processing unit configured to store and process respectively the time series.
  • the invention suggests a Cloud platform including i) memory means, ii) communication means configured to receive data from a plurality of loT devices, each loT device being associated with an underground water reservoir, and having output means configured to provide output to each loT device, which output is related to the underground water reservoir associated with said loT device, and iii) processing unit configured to analyze data employing cognitive techniques, such as Artificial Intelligence and Machine Learning techniques to provide trends of the behavior of water parameters of the underground water reservoirs, for example under pre-defined usage scenarios.
  • cognitive techniques such as Artificial Intelligence and Machine Learning techniques to provide trends of the behavior of water parameters of the underground water reservoirs, for example under pre-defined usage scenarios.
  • the Cloud platform may include a neural network or other Artificial Intelligence and Machine Learning techniques having an input layer configured to receive a dataset including data from said plurality of loT devices of the plurality of reservoirs.
  • the Cloud platform and a plurality of loT devices establish a system for monitoring the properties of underground waters.
  • a computer-implemented method which provides usage parameters, in particular pumping parameters, for a plurality of distinct wells, applies a cognitive technique and comprises: training the cognitive technique by data-sets, whereby each dataset contains data obtained from a plurality of loT devices, each loT device being associated with a distinct well of said plurality of wells.
  • the computer-implemented method includes a neural network with an input layer configured to receive said-data sets.
  • the computer-implemented method may be used for mapping a water table of a predefined geographical area.
  • a device operates a plurality of distinct pumping installations and includes a learning machine, e.g. a neural network, which provides pumping parameters and/or process optimization parameters to said installations.
  • the learning machine comprises an input stage configured to receive datasets, whereby each dataset contains data obtained from a plurality of loT devices, each loT device being associated with a distinct well.
  • the processing means of the Cloud platform includes particular means that may offer at least some of the following:
  • Each well may be associated with a pumping device or it may be used purely for other purposes, for example research and monitoring.
  • An example of a system according to the invention includes a plurality of loT devices.
  • Each loT device includes sensors, hardware with a memory unit, a processing unit and input means and output means. The sensors are appropriate to monitor
  • water parameters such as the Static Water Level (SWL), i.e. , the distance from the ground of the surface of underground waters local reservoir, the quality, temperature, pH, electrical-conductivity, Total Dissolved Solids (TDS), Oxidation-Reduction Potential (ORP), Dissolved Oxygen (DO)
  • SWL Static Water Level
  • TDS Total Dissolved Solids
  • ORP Oxidation-Reduction Potential
  • DO Dissolved Oxygen
  • pumping parameters such as Pumping Water Level (PWL), pumping times and durations, Drawdown (DD), time of water level stabilization, pumping rate, pumped quantity
  • Controlling parameters such as loT device health-check parameters, diagnostics and process-optimization parameters
  • Parameters are measured directly by the sensors or deduced from measured data.
  • the data including the directly measured parameters, are measured over time with a steady or variable frequency.
  • the measured data may form data series, for example water level vs. time, which subsequently are used in the analysis.
  • Each loT device is installed to a distinct reservoir.
  • Distinct underground waters reservoirs are reservoirs from which water is pumped via distinct water pump systems, such as wells. In general, these reservoirs may have solid boundaries therebetween. It may be that distinct reservoirs are indirectly connected, for example through subterranean rivers.
  • the monitoring parameters such as the frequency of the measurements may be adapted so as to optimize the energy consumption of the loT device, as well as the amount and value of data stored and processed. The parameters are selected using data analysis and ML.
  • the measurements are stored and processed locally in the memory unit of the loT device and, consequently, uploaded to the Cloud.
  • Appropriate means, software and/or hardware means process the data to obtain computed parameters that reflect the condition of the respective reservoir. These means include Cognitive techniques, such as ML and Al techniques as well as other in-house developed and state of the art algorithms and/or semi-conductors.
  • Parameters that are measured directly or induced from the measurements may include parameters related to the reservoir’s water capacity, the limits and rate of drainage and refill of the well along their seasonality, the induced information regarding the consistency, porosity and permeability of the well’s lower rocky surroundings.
  • the loT devices are registered to and interact with a Cloud-based Software as a Service platform (SaaS).
  • SaaS Cloud-based Software as a Service platform
  • the platform is equipped with modules that process the measurements and the derived parameters, which are inputted from the loT devices.
  • Measurements that are obtained from the loT devices and respective derived parameters are forwarded to the Cloud platform either real-time or at pre-defined or ad-hoc, for example in case of communication problems’ situations, intervals.
  • the data received from the loT devices are processed by the Cloud platform to provide insights and visual representations of the information. They may be also used to train the Al and ML modules in order to provide trends related to the parameters and qualities of the underground waters, such as the sustainability and seasonality of the well attributes and qualities and of the corresponding reservoir. Once trained the Al and/or ML modules may be employed to suggest optimal configuration and usage values, e.g. pumping times and duration, optimal irrigation parameters, i.e.
  • the process of the data by ML and Al techniques detect and reveal dependencies between the data of the wells of a reservoir of the plurality of the reservoirs and the data of the others of the plurality of the reservoirs.
  • the Al and ML modules of the Cloud platform are trained by records of datasets obtained from the loT devices. The records include data from a plurality of loT devices, so as to reveal hidden dependencies between the plurality of the reservoirs.
  • the ML and Al modules of the Cloud platform may be employed to simulate the behavior of the reservoirs under specific usage and environmental scenarios, so as to determine the appropriate parameters for future usage.
  • the appropriate usage parameters are transmitted via appropriate means to the loT devices and/or to the users of the system.
  • the Al and ML modules of the Cloud platform may define the effect that a usage scenario of a single reservoir or of more than one reservoir may have on the plurality of reservoirs that are registered to the platform.
  • the Cloud platform may also include a module to process the data received to provide several visualization aspects, including a geographic map depicting the underground water reservoir upper levels and other relevant attributes and qualities, e.g., water quality parameters and trends.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • General Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Medical Informatics (AREA)
  • Analytical Chemistry (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Molecular Biology (AREA)
  • Multimedia (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Biomedical Technology (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geophysics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)

Abstract

Plate-forme en nuage comprenant : i) des moyens de mémoire ; ii) des moyens de communication conçus pour recevoir des données en provenance d'une pluralité de dispositifs IoT, chaque dispositif IoT étant associé à un puits distinct, et des moyens de sortie conçus pour fournir une sortie à chaque dispositif IoT, laquelle sortie est associée au réservoir d'eau souterraine associé audit dispositif IoT : et iii) une unité de traitement conçue pour analyser des données à l'aide de techniques cognitives, telles que des techniques d'intelligence artificielle et d'apprentissage automatique et, le cas échéant, des techniques d'analyse statistique et/ou des techniques d'analyse de signal, et conçue pour fournir des tendances du comportement des paramètres d'eau des réservoirs d'eaux souterraines, par exemple dans des scénarios prédéfinis d'utilisation. La plate-forme en nuage et une pluralité de dispositifs IoT établissent un système de surveillance des propriétés d'eaux souterraines.
PCT/IB2021/056884 2020-07-30 2021-07-28 Plate-forme en nuage pour réservoirs d'eaux souterraines Ceased WO2022024009A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP21763405.4A EP4189443A1 (fr) 2020-07-30 2021-07-28 Plate-forme en nuage pour réservoirs d'eaux souterraines

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GR20200100453 2020-07-30
GR20200100453A GR20200100453A (el) 2020-07-30 2020-07-30 Πλατφορμα στο υπολογιστικο νεφος για υπογεια υδατα

Publications (1)

Publication Number Publication Date
WO2022024009A1 true WO2022024009A1 (fr) 2022-02-03

Family

ID=77595590

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2021/056884 Ceased WO2022024009A1 (fr) 2020-07-30 2021-07-28 Plate-forme en nuage pour réservoirs d'eaux souterraines

Country Status (3)

Country Link
EP (1) EP4189443A1 (fr)
GR (1) GR20200100453A (fr)
WO (1) WO2022024009A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114611778A (zh) * 2022-03-04 2022-06-10 山东锋士信息技术有限公司 一种基于入库流量的水库水位预警方法及系统
CN116956673A (zh) * 2023-07-20 2023-10-27 郑州大学 基于多参数概率分布的地下水流场模型优化方法及系统

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6356205B1 (en) * 1998-11-30 2002-03-12 General Electric Monitoring, diagnostic, and reporting system and process
TW201235969A (en) * 2011-02-24 2012-09-01 Jnc Technology Co Ltd Cloud monitoring method for underground water and system thereof
US20140195174A1 (en) * 2013-01-09 2014-07-10 David W. Machuga Well water and aquifer quality measurement and analysis system

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018236238A1 (fr) * 2017-06-20 2018-12-27 Schlumberger Technology B.V. Prédiction de performances d'écoulement de puits de forage
CN108055318A (zh) * 2017-12-11 2018-05-18 江苏卓尔美物联科技股份有限公司 一种水环境物联网方法与装置
CN108756848B (zh) * 2018-05-21 2019-05-21 北京四利通控制技术股份有限公司 一种智能钻井控制云平台及智能钻井控制系统
CN108776465A (zh) * 2018-06-12 2018-11-09 中国地质调查局南京地质调查中心 基于物联网的地下水质监测系统和主系统
KR102015940B1 (ko) * 2019-04-24 2019-08-28 주식회사 효림 무전원 지하수 특성 자동측정 데이터로거를 이용한 지하수 이동 분포 모니터링 시스템
CN110048894A (zh) * 2019-04-24 2019-07-23 广东省智能机器人研究院 一种用于油气生产的多井数据采集与智能监控方法及系统

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6356205B1 (en) * 1998-11-30 2002-03-12 General Electric Monitoring, diagnostic, and reporting system and process
TW201235969A (en) * 2011-02-24 2012-09-01 Jnc Technology Co Ltd Cloud monitoring method for underground water and system thereof
US20140195174A1 (en) * 2013-01-09 2014-07-10 David W. Machuga Well water and aquifer quality measurement and analysis system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114611778A (zh) * 2022-03-04 2022-06-10 山东锋士信息技术有限公司 一种基于入库流量的水库水位预警方法及系统
CN114611778B (zh) * 2022-03-04 2022-09-06 山东锋士信息技术有限公司 一种基于入库流量的水库水位预警方法及系统
CN116956673A (zh) * 2023-07-20 2023-10-27 郑州大学 基于多参数概率分布的地下水流场模型优化方法及系统

Also Published As

Publication number Publication date
GR20200100453A (el) 2022-02-11
EP4189443A1 (fr) 2023-06-07

Similar Documents

Publication Publication Date Title
US20230038850A1 (en) Flood monitoring and management system
JP7001626B2 (ja) 河川流域に関する水文解析および管理のためのプロセスおよびシステム
JP6900480B2 (ja) 地下水源から汲み上げ可能な水の最大許容量を経時的に決定する方法
CN116579128B (zh) 基于机器学习模型的工业园区地下水污染在线预测方法
CN107633321B (zh) 一种地下水资源智能管理系统及管理方法
AU2022259875A1 (en) System and method for cathodic protection monitoring
KR101947753B1 (ko) L-thia acn-wq 모델을 이용한 유역 오염부하량 평가 장치 및 그 방법
CN119935253B (zh) 植物生长监测及养护系统
WO2022024009A1 (fr) Plate-forme en nuage pour réservoirs d'eaux souterraines
CN120781437B (zh) 基于bim和gis的水利工程智能处理系统
CN120374143B (zh) 一种地下水污染智能监测与溯源方法及系统
Sene Hydrological forecasting
Piro et al. Smart and innovative systems for urban flooding risk management
Essamlali et al. A new architecture of Low Impact Development (LID)-based stormwater management system through Internet of Things (IoT) and Machine Learning integration
CN112906239B (zh) 综合管廊的安全性态评价方法、装置、处理器及存储介质
Karst et al. On the effect of nonlinear recessions on low flow variability: Diagnostic of an analytical model for annual flow duration curves
CN120354787A (zh) 一种基于数字孪生的河道水情预报方法及系统
Agheli et al. Modeling the spatial dependence of quality data using distance-based inputs and a data-driven model
NAM et al. Groundwater modeling for groundwater management in the coastal area of Mekong Delta, Vietnam
Jagtap et al. Monitoring of groundwater level and development of control mechanism based on machine learning algorithm
Zurian et al. Automated monitoring of natural parameters of renewable energy production systems
Voichur et al. Method of operation of the cyber-physical water resources monitoring system
Yu et al. Multi-objective Optimization for Island Groundwater Management: Efficient Training of Gaussian Process Models
Dhatterwal et al. IoT Applications in Geotechnical Engineering
Shen et al. Innovation and practical application of artificial intelligence technology in water conservancy engineering

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21763405

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2021763405

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2021763405

Country of ref document: EP

Effective date: 20230228

NENP Non-entry into the national phase

Ref country code: DE