Sustainable Monitoring System Research Articles

MT-InSAR Optimisation for Structural Health Monitoring

The implementation of effective and sustainable Structural Health Monitoring (SHM) systems for the evaluation of infrastructure conditions is critical to address the deterioration and damage experienced by structures worldwide. Given the vast number of structures involved, resorting to traditional in-situ visual inspections and data gathering methods is becoming increasingly unfeasible. Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has recently gained attention as a viable solution for long-term SHM. This remote sensing technique combines multiple satellite radar images to measure changes in the Earth’s surface over time. Unlike conventional techniques, MT-InSAR does not require in-situ installations and offers extensive coverage, enabling observations across diverse location and structures. However, the applicability of MT-InSAR monitoring depends on the relatively unpredictable distribution and location of permanent scatterers (PSs), which are influenced by surface characteristics and vegetation changes. Evaluating the reliability and capacity of MT-InSAR is therefore crucial to enhance its effectiveness in assessing the location and extent of structural damage. In this study, we present an effective approach to determine the optimal number and position of PSs for detecting different structural damage mechanisms. The approach is exemplified through a case study of a quay wall in Amsterdam, with data inputs simulated using the Finite Element Method. The proposed method has the potential to evaluate the feasibility of MT-InSAR for a broader range of scenarios, enabling to detect specific structural conditions.

Implementation of a sustainable real-time air quality monitoring system using the Internet of Things for Kaduna metropolis, Nigeria

Implementation of a sustainable real-time air quality monitoring system using the Internet of Things for Kaduna metropolis, Nigeria

A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things

Global population growth and increasing pollution levels are directly related. The effect does not just apply to outdoor spaces. Likewise, the low indoor air quality is also having a negative impact on the health of the building residents. According to the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Tackling this public health issue, due to the direct relationship between air pollution levels and mortality and morbidity rates as well as overall comfort, is mandatory. Many companies have begun to build inexpensive sensors for use in Internet of Things (IoT)-based applications to pollution monitoring. The research highlights design aspects for sustainable monitoring systems including sensor types, the selected parameters, range of sensors used, cost, microcontrollers, connectivity, communication technologies, and environments. The main contribution of this systematic paper is the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. Firstly, the IEEE database had the highest contribution to this research (48.51%). The results showed that 87.1%, 66.3%, and 36.8% of studies focused on harmful gas monitoring, thermal comfort parameters, and particulate matter levels pollution, respectively. The most studied harmful gases were CO2, CO, NO2, O3, SO2, SnO2, and volatile organic compounds. The cost of the sensors was suitable for people with limited incomes and mostly under USD 5, rising to USD 30 for specific types. Additionally, 40.35% of systems were based on ESP series (ESP8266 and ESP32) microcontrollers, with ESP8266 being preferred in 34 studies. Likewise, IoT cloud and web services were the preferred interfaces (53.28%), while the most frequent communication technology was Wi-Fi (67.37%). Indoor environments (39.60%) were the most studied ones, while the share for outdoor environments reached 20.79% of studies. This is an indication that pollution in closed environments has a direct impact on living quality. As a general conclusion, IoT-based applications may be considered as reliable and cheap alternatives for indoor and outdoor pollution monitoring.

Planar Thermoelectric Microgenerators in Application to Power RFID Tags.

This paper presents an innovative approach to the integration of thermoelectric microgenerators (μTEGs) based on thick-film thermopiles of planar constantan-silver (CuNi-Ag) and calcium cobaltite oxide-silver (Ca3Co4O9-Ag) thick-film thermopiles with radio frequency identification (RFID) technology. The goal was to consider using the TEG for an active or semi-passive RFID tag. The proposed implementation would allow the communication distance to be increased or even operated without changing batteries. This article discusses the principles of planar thermoelectric microgenerators (μTEGs), focusing on their ability to convert the temperature difference into electrical energy. The concept of integration with active or semi-passive tags is presented, as well as the results of energy efficiency tests, considering various environmental conditions. On the basis of the measurements, the parameters of thermopiles consisting of more thermocouples were simulated to provide the required voltage and power for cooperation with RFID tags. The conclusions of the research indicate promising prospects for the integration of planar thermoelectric microgenerators with RFID technology, opening the way to more sustainable and efficient monitoring and identification systems. Our work provides the theoretical basis and practical experimental data for the further development and implementation of this innovative technology.

Tracking Soil Moisture Levels and Automated Plant Watering System Utilizing IOT Technology

Nowadays, technology plays a tremendous role in serving humanity, and food is a man’s basic and primary need. Approximately 70% of Northern Nigerians are directly or indirectly dependent on agriculture. Water pump irrigation cannot be maintained due to frequent power outages, the inaccessibility of grid lines in remote areas, and the scarcity and cost of fuel to run pumps. This IoT-based automatic irrigation system is proposed to create a sustainable field monitoring system for improved crop growth and production. In this system, IOT and WSN control and monitor the irrigation system. IoT collects saved data and monitors diverse soil contents in real time. WSN is utilized to create a user-friendly wireless system for cultivating and irrigating the field. Sensors of several types are utilized. This article uses the “Thing Talk Cloud Server” to operate and monitor a fully automated drip irrigation system. The temperature and humidity of the soil are constantly monitored. The system notifies the user of any abnormal conditions, such as low moisture content, rising temperature, or water concentration, by sending notifications via the wireless module. This will significantly improve food security and the well being of the Nigerian populace all through the year while boosting the economy.

All‐Weather Self‐Powered Intelligent Traffic Monitoring System Based on a Conjunction of Self‐Healable Piezoresistive Sensors and Triboelectric Nanogenerators

AbstractA self‐powered and sustainable traffic monitoring system is highly required for future urban development. Herein, self‐healable piezoresistive sensors and triboelectric nanogenerators (TENGs) are constructed by in situ polymerization of polyvinyl alcohol‐polyacrylamide double network hydrogel in the presence of sodium alginate and tannic acid‐modified cellulose nanocrystals (denoted as PPC) for all‐weather self‐powered intelligent traffic monitoring applications. Because of hydrogen bonding and boron ester bonding, the resultant PPC‐based strain sensor can rapidly self‐heal and restore its sensing ability within 1 min with a self‐healing efficiency of 97.4%. Based on piezoresistive effect, the ions in sodium alginate endow the PPC‐based strain sensor with a relatively high gauge factor of 8.39, which can monitor the motion and fatigue of drivers. Based on triboelectrification effect, the PPC‐based TENG sensor can detect instantaneous vehicle speed, judge traffic accident liability, evaluate vehicle weight, and alert the driver to prevent accidents caused by drowsy driving. After partially replacing water in the PPC with glycerin, the resulting PPC‐based TENG sensor exhibits stable performance at temperatures ranging from ‐30 to 40 °C, ensuring its all‐weather monitoring ability. The all‐weather and self‐powered intelligent traffic monitoring system is promising for ensuring the security of future cities.

Smarthub for supervising system for resource exploration and pollution control in deep-water and coastal areas based on ICT technologies

PurposeThis study aims to explore the need for highly technological complexes for control and monitoring, as well as, new concepts and methodologies for maritime resource exploration and exploitation, which are in great demand nowadays.Design/methodology/approachThis paper provides an analysis of demand, means of creation and development of the methodology and infrastructure for global monitoring, pollution control and supervision of smart systems for activities in exploration, future resource exploitation in deep-water and coastal areas based on Smarthub architecture, Unmanned Aircraft System (UAS), Continuous Acquisition and Life-Cycle Support (CALS) and Blockchain technologies.FindingsObservational, experimental, simulation, derivational, hybrid descriptive and analytical models, as well as, surrogate models were created, analyzed and implemented for assigned tasks realization. Concept of distributed system for marine environmental monitoring, control and supervising as pilot technology in the context of Technology Readiness Levels (TRL) 3–5 was designed and evaluated.Originality/valueThe activities described in this article should be realized in the design and development of a complex, reliable, robust and sustainable monitoring and inspection system for the control and evaluation of the impact and risk assessment of the exploration and future exploitation of maritime resources.

A tree-level analysis of baboon damage in commercial forest stands using deep learning techniques

Commercial forest plantations in South Africa are homogeneous monocultures of highly bred exotic species grown to deliver timber products of the best potential quality. As such, these stands are susceptible to adverse effects of biotic and abiotic factors, and therefore require intense management to mitigate these risks. A sustainable forest monitoring system that can detect real-time changes in the physiological state of these plantations is needed for timeous management intervention to reduce losses. The use of machine learning algorithms has recently become popular, with acceptable levels of success. This study explores the application of deep learning neural networks for early detection of damage caused by baboons in evergreen plantations of Pinus species. Using PlanetScope imagery (spectral band 590–860 nm), which is captured by a constellation of Dove nanosatellites, with a high temporal resolution available daily at 3 m spatial resolution, the study achieved an overall accuracy of 81.54%, with a kappa value of 0.69, using a deep neural network. In comparison, using a random-forest classifier produced 74.04% accuracy and a kappa value of 0.62. The study successfully mapped different levels of baboon damage within commercial pine forests. We provide a repeatable method for daily monitoring initiatives, and attest to the utility of higher-resolution imagery such as PlanetScope for mapping health and damage severity at the tree level.

Smart Sustainable Water Monitoring System via Internet of Things (IoT) for Water Retention Pond UiTM Cawangan Johor, Kampus Pasir Gudang

Water retention ponds developed in UiTM Pasir Gudang is used to accommodate the overflowed water from the campus facilities. Overflowed water is accumulated into this water retention pond and will flow to other water sources. Water from the water retentionpond has potential to contaminate other water sources as well as the environment. To maintain the water retention pond, a sustainable water monitoring system with an integration of internet of things (IoT) technology is designed and implemented. A fully developed integrated IoT monitoring system is presented in this research paper. The developedsystem will assess the water parameter such as pH and water temperature as well as water quality to serve as the water health indicator. This system includes a pH sensor, temperature sensor, rain sensor, sustainable power supply, wireless frequency data transmission module, and IoT data acquisition system in the base station. After the water data process has been performed and verified, the authorities will be notified of the unhealthy water condition. The developed system also allows any person or authority to monitor the water quality in that environment from anywhere.As a result of the collected data for water data parameters, the developed system provides an efficient water parameter monitoring system.Thus, early actions can be taken to reduce any harmful effects to the environment due to poor intervention.

MedGini: Gini index based sustainable health monitoring system using dew computing

Monitoring biosignals is crucial for intelligent health applications. Internet of Health Things (IoHT) provides a new path for monitoring the biosignals. Environment adaptive data dissemination is the primary requirement for the deployment of time and space-efficient monitoring systems. Existing dew-based systems lack an opportunistic architecture of data-synchronization with the cloud. This paper proposes a model that makes efficient use of IoT and cloud-dew architecture for a sustainable health monitoring system. Wireless sensor nodes are used to monitor the biosignals dynamically. All accrued data is temporarily stored in the dew layer. It is synchronized with the cloud at a subsequent phase to achieve seamless accessibility and optimal scalability of the data. Data synchronization plays an essential role in the cloud dew framework. We have used the Gini index and Shannon entropy to ensure intelligent data synchronization with the cloud. Sometimes sensors produce erroneous data, which poses a significant threat to the sustainable health monitoring system. Fuzzy normal distribution with a triangular membership function has been used to clean up the data and filter out the outliers. Further, we compared the proposed MedGini model with the existing models and analyzed the system performance. MedGini is found to outperform others concerning cost and power consumption.

IoT Based Temperature and Soil Monitoring System with Motor Pump Control

Abstract: To serve the humanity nowadays technology is playing an exquisite role and a man’s basic and first need is food indeed. It will be said that about over 85% of individuals of India are directly, indirectly relied on agriculture. Proper irrigation by pump can not be maintained because of frequent power outages, unavailability of grid lines in remote areas and scarcity/cost of fuel to run pumps. to create the sustainable irrigation system and field monitoring system for convalescing crops growth also as best production, this IOT based Automatic irrigation system is proposed. during this system IOT and WSN are wont to control and monitor the irrigation system. IOT is employed to get stored data monitoring and real time monitoring of varied contents of soil. WSN is employed to create a totally wireless system to form a user-friendly system to cultivate and irrigate water properly to the sphere. Different types of sensors are used. This report presents a totally automated drip irrigation system which is controlled and monitored by using “Thinks peak Cloud Server”. Temperature and also the humidity content of the soil are frequently monitored. The system informs user about any abnormal conditions like less moisture content and temperature rise, even concentration of water by sending notifications through the wireless module.

A pilot project for the long-term structural health monitoring of historic city gates

This paper reports on a pilot project for long-term structural health monitoring of historical city gates. This structural typology is what today remains of the defensive structures that characterised the medieval centres of many European cities. Even though in the last years the scientific community has paid great attention to the structural analysis and on the structural health monitoring of masonry towers, which are apparently similar to the typology of city gates, only a few studies explicitly approach such heritage buildings. In most cases, the surviving gates are immersed in the daily vehicular-traffic flows and hence continuously subjected to traffic-induced vibration. Consequently, a key point both for their conservation is the assessment of the optimal sensors position to be used for reliable structural health monitoring procedures. These issues are here discussed with reference to a specific case: the San Niccolò gate in Florence (Italy). First, an expeditious experimental campaign performed with a reduced number of accelerometers is reported. This test is aimed to identify a numerical model that is subsequently employed to design the optimal sensor position of long-term structural monitoring. This optimal sensor grid must be assessed to ensure a low-cost and sustainable dynamic monitoring system but, at the same time, to maximize the information contents.

제5차 국민건강증진종합계획(HP2030)에서의 ‘건강형평성’ 목표: HP2020에 대한 평가와 국외 사례고찰을 바탕으로

Objectives: This study was conducted to enhance our understanding and execution of the National Health Plan 2030 (HP2030) by analyzing its health equity goals, based on an evaluation of the National Health Plan 2020 (HP2020) and international cases. Methods: The degree of change in the health gap based on gender, income, and region of the leading health indicators in HP2020 was analyzed. In addition, the National Health Plans of the United States, China, and Japan were comparatively analyzed from the perspective of health equity. Finally, health equity in the HP2030 was analyzed focusing on vision, basic principles, performance indicators, and target values. Results: Health disparities according to gender, income, and region were observed for all indicators of the HP2020. Moreover, for the United States, China, and Japan, there was a common emphasis on data monitoring, to identify vulnerable groups. Furthermore, they focused on social and national responsibilities for improving health levels and achieving health equity. In the HP2030, it was confirmed that health equity was considered for the entire process of planning, including the selection of vision, basic principles, key projects, performance indicators, and target values. Conclusion: For the success of the HP2030, it is necessary to establish an effective action plan, strengthen the policy feedback function, improve communication and legal systems for linkage and cooperation, and establish a sustainable monitoring system.

IOT BASED SOIL MONITORING AND AUTOMATIC IRRIGATION SYSTEM IN THE RURAL AREA OF BANGLADESH

To serve the humanity nowadays technology is playing a wonderful role and a man’s basic and primary need is food indeed. It can be said that about more than 85% of people of Bangladesh are directly, indirectly depended on agriculture. Proper irrigation by water pump cannot be maintained due to frequent power outages, unavailability of grid lines in remote areas and scarcity/cost of fuel to run pumps. To make the sustainable irrigation system and field monitoring system for getting better crops growth as well as best production, this IOT based Automatic irrigation system is proposed. In this system IOT and WSN are used to control and monitor the irrigation system. IOT is used to obtain stored data monitoring and real time monitoring of various contents of soil. WSN is used to make a fully wireless system to make a user-friendly system to cultivate and irrigate water properly to the field. Different kinds of sensors are used. This report presents a fully automated drip irrigation system which is controlled and monitored by using “Thinkspeak Cloud Server”. Temperature and the humidity content of the soil are frequently monitored. The system informs user about any abnormal conditions like less moisture content and temperature rise, even concentration of water by sending notifications through the wireless module.

Self-Powered Low-Cost UVC Sensor Based on Organic-Inorganic Heterojunction for Partial Discharge Detection.

Power outages caused by the aging of high-voltage power facilities can cause significant economic and social damage. To prevent such problems, it is necessary to implement a widespread and sustainable monitoring system. Partial discharge (PD) is a preliminary symptom of power equipment aging accompanying the light, typically in the UV range. UVC (200-280nm) is more useful than UVA and UVB because of low interference from the environment owing to its solar-blindness by the stratosphere. Therefore, to realize a wide-range and durable diagnosis system, it is necessary to develop sensors that can selectively detect UVC, while enabling mass production at low-cost and low power consumption. Here, a solution-processable photodiode sensor that is inexpensive, mass-producible, and self-powered with selective UVC detection is developed. The optoelectronic characteristics of photodiode consisting of organic p-polymer and inorganic n-ZnO nanoparticles are systematically studied to determine the optimum p-type polymer and its thickness. The device shows high-performance: fast response time (rise/fall time: 36.6/37.0ms) and high spectral response in the UVC region (maximum responsivity of 20mA W-1 ) under self-powered operation. Furthermore, the practical application of the device to detect PD signals with a visual alarm system under UVC release conditions is demonstrated.

Evaluating Large-Scale Integrated Care Projects: The Development of a Protocol for a Mixed Methods Realist Evaluation Study in Belgium.

Background:The twelve Integrated Care Program pilot projects (ICPs) created by the government plan ‘Integrated Care for Better Health’ aim to achieve four outcome types (the Quadruple Aim) for people with chronic diseases in Belgium: improved population health, improved patient and provider experiences and improved cost efficiency. The aim of this article is to present the development of a mixed methods realist evaluation of this large-scale, whole system change programme.Methods:A scientific team was commissioned to co-design and implement an evaluation protocol in close collaboration with the government, the ICPs and several other involved stakeholders.Results:A protocol for a mixed methods realist evaluation was developed to gain insights into the mechanisms that foster successful results in ICPs. The qualitative evaluation proposed will be based on the document analysis of yearly ICP progress reports, selected case studies and focus group interviews with stakeholders. Processes and outcomes of all the projects will be monitored using indicators based on administrative data on population health and the quality and costs of care. A yearly survey will be organized to collect data on patient-reported outcomes and experiences and on provider-reported measures of inter-professional collaboration and proper wellbeing. Using both quantitative and qualitative data, we will develop theories about the mechanisms and the associated contextual factors that lead to integrated care and the Quadruple Aim outcomes.Discussion:The objective of this study is to deliver policy recommendations on strategies and best practices to improve care integration in Belgium and to implement a sustainable monitoring system that serves both policy makers and the stakeholders within the ICPs. Some challenges due to the large scale of the project and the multiple stakeholders involved may impede the successful implementation of this proposal.

Sustainable Smartphone-Based Healthcare Systems: A Systems Engineering Approach to Assess the Efficacy of Respiratory Monitoring Apps

Recent technological developments along with advances in smart healthcare have been rapidly changing the healthcare industry and improving outcomes for patients. To ensure reliable smartphone-based healthcare interfaces with high levels of efficacy, a system dynamics model with sustainability indicators is proposed. The focus of this paper is smartphone-based breathing monitoring systems that could possibly use breathing sounds as the data acquisition input. This can especially be useful for the self-testing procedure of the ongoing global COVID-19 crisis in which the lungs are attacked and breathing is affected. The method of investigation is based on a systems engineering approach using system dynamics modeling. In this paper, first, a causal model for a smartphone-based respiratory function monitoring is introduced. Then, a systems thinking approach is applied to propose a system dynamics model of the smartphone-based respiratory function monitoring system. The system dynamics model investigates the level of efficacy and sustainability of the system by studying the behavior of various factors of the system including patient wellbeing and care, cost, convenience, user friendliness, in addition to other embedded software and hardware breathing monitoring system design and performance metrics (e.g., accuracy, real-time response, etc.). The sustainability level is also studied through introducing various indicators that directly relate to the three pillars of sustainability. Various scenarios have been applied and tested on the proposed model. The results depict the dynamics of the model for the efficacy and sustainability of smartphone-based breathing monitoring systems. The proposed ideas provide a clear insight to envision sustainable and effective smartphone-based healthcare monitoring systems.

Wireless Sensor Networks for Continuous Structural Health Monitoring of Historic Masonry Towers

ABSTRACT The recent developments of micro-electro-mechanical systems and wireless sensor networks allow today the use of low-cost and small-size sensors for continuous monitoring of civil structures. Both these features are very important for the low impact of the sensor grid in heritage structures, ensuring a low-cost and sustainable dynamic monitoring system. Over the last 20 years the use of sensor networks for continuous monitoring has received a growing interest. Anyway, still numerous questions remain opened about the sensitivity of measurement devices, the optimization of number and positioning of sensors, the energy efficiency of the network, and the development of algorithms for real-time data analysis. This paper, based on the aforementioned motivations, discusses about a monitoring system made of micro-electro-mechanical sensors connected through a wireless network. The architecture of the wireless sensor network and the automatized procedure proposed for the continuous processing of the recorded signals are discussed and described with reference to an explicative masonry tower case study. It is believed that the proposed technologies can provide an economical and relatively non-invasive tool for real-time structural monitoring and that, moreover, the availability of large amounts of data from actual measurements can give effective information on the structural behaviour of historic constructions.

Impact on Readmission Reduction Among Heart Failure Patients Using Digital Health Monitoring: Feasibility and Adoptability Study.

BackgroundHeart failure (HF) is a condition that affects approximately 6.2 million people in the United States and has a 5-year mortality rate of approximately 42%. With the prevalence expected to exceed 8 million cases by 2030, projections estimate that total annual HF costs will increase to nearly US $70 billion. Recently, the advent of remote monitoring technology has significantly broadened the scope of the physician’s reach in chronic disease management.ObjectiveThe goal of our program, named the Heart Health Program, was to examine the feasibility of using digital health monitoring in real-world home settings, ascertain patient adoption, and evaluate impact on 30-day readmission rate.MethodsA digital medicine software platform developed at Mount Sinai Health System, called RxUniverse, was used to prescribe a digital care pathway including the HealthPROMISE digital therapeutic and iHealth mobile apps to patients’ personal smartphones. Vital sign data, including blood pressure (BP) and weight, were collected through an ambulatory remote monitoring system that comprised a mobile app and complementary consumer-grade Bluetooth-connected smart devices (BP cuff and digital scale) that send data to the provider care teams. Care teams were alerted via a Web-based dashboard of abnormal patient BP and weight change readings, and further action was taken at the clinicians’ discretion. We used statistical analyses to determine risk factors associated with 30-day all-cause readmission.ResultsOverall, the Heart Health Program included 58 patients admitted to the Mount Sinai Hospital for HF. The 30-day hospital readmission rate was 10% (6/58), compared with the national readmission rates of approximately 25% and the Mount Sinai Hospital’s average of approximately 23%. Single marital status (P=.06) and history of percutaneous coronary intervention (P=.08) were associated with readmission. Readmitted patients were also less likely to have been previously prescribed angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (P=.02). Notably, readmitted patients utilized the BP and weight monitors less than nonreadmitted patients, and patients aged younger than 70 years used the monitors more frequently on average than those aged over 70 years, though these trends did not reach statistical significance. The percentage of the 58 patients using the monitors at least once dropped from 83% (42/58) in the first week after discharge to 46% (23/58) in the fourth week.ConclusionsGiven the increasing burden of HF, there is a need for an effective and sustainable remote monitoring system for HF patients following hospital discharge. We identified clinical and social factors as well as remote monitoring usage trends that identify targetable patient populations that could benefit most from integration of daily remote monitoring. In addition, we demonstrated that interventions driven by real-time vital sign data may greatly aid in reducing hospital readmissions and costs while improving patient outcomes.

AN OPEN DROUGHT MONITORING SYSTEM FOR THE DEDURU OYA BASIN IN SRI LANKA IN THE CONTEXT OF THE 4ONSE PROJECT

Abstract. During the last decade, many climatic parameters are more and more deviating from the mean values calculated over historical climatic time-series. The World Meteorological Organization (WMO) stated that years 2015, 2016 and 2017 were the warmest since 1850. According to the preliminary data analysis of the last year, this trend is going to continue in 2018 as well. These climatic changes have accelerated the occurrence of droughts in many parts of the world including Europe and Asia. The North Western region is one such region in Sri Lanka which always affected by droughts due to its inherent dry weather condition. Although drought is considered as a normal part of the climate, its increasing negative impacts on human activities and the environment urges the application of novel technologies in drought monitoring. The 4onse project (analysis of Open, Non-conventional, Sustainable and Effective monitoring systems), funded by the Swiss National Science Foundation (SNSF), is a project began in this context to address the aforementioned mentioned issues by deploying 27 open technologies based Environmental Monitoring Systems (EMS) in Deduru Oya basin of Sri Lanka. These systems measure weather parameters such as temperature, humidity, pressure, rainfall, wind speed and wind direction. Thanks to the collected data, it was possible to calculate some drought indexes to evaluate the intensity of the dry periods. The Standard Precipitation Index (SPI) has been selected as the principal indicator to evaluate droughts by integrating the 4onse data together with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset.