A Concept Paper on Smart River Monitoring System for Sustainability in River

Elijah, Olakunle; Rahman, Tharek Abdul; Leow, Chee Yen; Yeen, H. C.; Sarijari, Mohd Adib; Aris, Azmi; Salleh, Jamaliah; Chua, Tien Han

doi:10.30880/ijie.2018.10.07.012

Cited by 15 publications

(15 citation statements)

References 19 publications

(2 reference statements)

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“…Agriculture (Hasni et al, 2022); (Yusof et al, 2022); (Liu et al, 2021); (Rafezall et al, 2020); (Izzuddin et al, 2020); (Avtar et al, 2020); (Syafiqah et al, 2020); (Abdullah et al, 2020); (Fawcett et al, 2019); (Suab et al, 2019); (Ismail et al, 2019); (Ali et al, 2018); (Norasma et al, 2018) Mapping (Zolkepli et al, 2021); (Chaudhry et al, 2020); (Noor et al, 2020); (Kaamin et al, 2020); (Udin et al, 2019); (Yazid et al, 2019); (Ahmad et al, 2018a); (Abdullah et al, 2018); (Yusoff et al, 2018); (Ahmad et al, 2018b) Forestry (Siti-Nor-Maizah et al, 2022); (Kamarulzaman et al, 2022); (Saliu et al, 2021); (Maizah Saad et al, 2020); (Otero et al, 2018); (Nadia Zainol et al, 2018) River monitoring (Ansari et al, 2021); (Mohamad et al, 2019); (Geraeds et al, 2019); (Elijah et al, 2018a); (Elijah et al, 2018b) Transportation (Zailani et al, 2021); (Mahmud et al, 2021); (Sanik et al, 2019); (Abdullah et al, 2019) Construction (Keyvanfar et al, 2022a); (Keyvanfar et al, 2022b); (Mohd Noor et al, 2020); …”

Section: Application Referencesmentioning

confidence: 99%

Recent Drone Applications in Malaysia: An Overview

Gohari

Ahmad

Oloruntobi

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This study aims on an exploration of recent applications of drones in the Malaysian context. More specifically, to investigate the drone’s application status and application areas. To search the academic literature and identify the included documents, we have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We conducted an overview study of the total number of 54 documents (out of 202) published since 2018, which were extracted from the Scopus database. The results of the bibliometric analysis of the included documents showed that drones in Malaysia are applied in different disciplines, including agriculture, mapping, forestry, river monitoring, transportation, construction, coastal management, flood management, and other applications. Among these, agriculture and mapping are the main application areas. MDPI published the most journal articles, and IGRSM conferences published the highest number of conference papers. Furthermore, scholars from the Universiti Teknologi Malaysia have the highest contribution among others.

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Section: Application Referencesmentioning

confidence: 99%

Recent Drone Applications in Malaysia: An Overview

Gohari

Ahmad

Oloruntobi

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Although UAS enhance the reliability of data, there are still barriers to their full integration in limnological studies; the drone s maximum carrying capacity limits the number and volume of samples, making these less representative of water chemistry compared to manually collected samples. Elijah et al [148] implemented a smart river monitoring system that included an UAS to collect water samples and was equipped with and array of sensors and probes. Koparan et al [149] designed a lightweight hexacopter equipped with a multiprobe system to measure temperature, electrical conductivity (EC), dissolved oxygen (DO), and pH.…”

Section: Water Sampling and In Situ Parameter Measurementmentioning

confidence: 99%

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

et al. 2021

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In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs.

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“…Several techniques were developed for remote detection and identification of algal blooms. For example, in situ sensors and low-cost sensors were developed for algae detection [48], [49], [50], [51], [47], [52]. These sensors replace human effort for water sampling as well as human observation (judgment).…”

Section: B Monitoring and Profiling Water Quality For Growth Of Algaementioning

confidence: 99%

“…The authors mentioned that the same approach could be used in an array to help detect HABs. Finally, a concept paper was proposed [52] on the Smart River Monitoring System for river sustainability. Inspired by these studies, additional modifications to the hardware to suit the low-cost concept for seawater monitoring system was studied and proposed in our previous work [3].…”

Section: B Monitoring and Profiling Water Quality For Growth Of Algaementioning

confidence: 99%

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

et al. 2021

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An end-to-end process to achieve a complete framework methodology for Harmful Algal Bloom (HAB) growth prediction is crucial for water management, especially in implementing robust predictive modelling of HAB to prevent water pollution. Previous works have separately focused on the prediction part or the implementation of the water monitoring system that involves the integration of sensors through the Internet of Things (IoT). These studies lack in terms of discussion of both IoT with the algae ecological domain and prediction method. Therefore, this paper takes the initiative to provide a wider coverage on the end-to-end process including the assembly and integration of sensors, data acquisition and predictive modelling using data-driven approaches, for example, machine learning, deep learning and deep time series forecasting algorithm for future algal bloom outbreak mitigation. This paper believes that discussion in a complete framework perspective based on the execution of each phase is important besides providing a true understanding of the algae growth factors and prediction problems to achieve a robust prediction algorithm for algal growth. In the end, this paper presents proof that selecting the right features and utilising time series with deep learning are much better for tackling the issues of highly non-linear and dynamic algae ecological data that are briefly introduced in this paper. Among all the algorithms selected, Long Short-term Memory (LSTM) is the best fit for the prediction method and has outperformed other basic machine learning methods in accurately predicting algal growth through the prediction of chlorophyll-a (Chla) as a strong indicator of algal presence for coastal studies.

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A Concept Paper on Smart River Monitoring System for Sustainability in River

Cited by 15 publications

References 19 publications

Recent Drone Applications in Malaysia: An Overview

Recent Drone Applications in Malaysia: An Overview

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

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