Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches

Latif, Sarmad Dashti; Alyaa Binti Hazrin, Nur; Hoon Koo, Chai; Lin Ng, Jing; Chaplot, Barkha; Feng Huang, Yuk; El-Shafie, Ahmed; Najah Ahmed, Ali

doi:10.1016/j.aej.2023.09.060

Cited by 30 publications

(6 citation statements)

References 35 publications

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“…The application of ML methods based on the Artificial Neural Networks (ANNs) applied to Remote Sensing (RS) data processing considerably increases the effectiveness of mapping [56,57]. Advanced ML methods enable the landscape dynamics of spatial and temporal trends to be automatically revealed through computer-based algorithms of pattern recognition and data analysis, as reported in existing studies [58][59][60][61][62]. Several ML and AI algorithms exist to analyze and quantify spatial data using analytical and empirical approaches.…”

Section: Theoretical Framework and Motivationmentioning

confidence: 99%

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Lemenkova

2024

JMSE

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This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

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Section: Theoretical Framework and Motivationmentioning

confidence: 99%

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Lemenkova

2024

JMSE

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“…Initially, the application of machine learning in coastal simulation faced limitations due to the challenge of obtaining oceanic data. However, recent progress in satellite remote sensing and unmanned aerial vehicle observation has alleviated these data constraints [36][37][38]. For instance, Nagur Cherukuru et al [39] developed a semi-analytical remote sensing model, facilitating the retrieval of suspended sediment and dissolved organic carbon in coastal waters.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Yan,

Zhang,

et al. 2024

JMSE

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Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.

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“…In addition to the sectors above, it turns out that rainfall can also have an impact on several sectors such as agriculture, fisheries, transportation, and tourism (Fransiska et al, 2019). As in agriculture, which will have an impact on crop failure due to drought or flooding (Kartiasih & Setiawan, 2019;Latifa et al, 2023;Yuliana et al, 2023;Latif et al, 2023). In the fisheries sector, which will affect the results that will be obtained by fishermen.…”

Section: Introductionmentioning

confidence: 99%

Utilizing the Sarima Model and Support Vector Regression to Forecast Monthly Rainfall in Bandung City

Innayah,

Sulistiana,

Febrian

et al. 2024

jitter

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As one of the largest cities in Indonesia, Bandung has varying monthly rainfall intensity. High rainfall is very dangerous for people's lives and will have an impact on various sectors such as agriculture, fisheries, tourism, and transportation. For this reason, rainfall prediction is needed as an effort for the government to make policies and the community can anticipate the possibility of high rainfall that occurs. This study compares the effectiveness of SARIMA and Support Vector Regression (SVR) models in predicting monthly rainfall objectively, with the aim of improving decision making for stakeholders. Forecasting rainfall data is carried out based on the best method of the two methods that have been compared. The results showed that the SARIMA method outperformed the SVR method in forecasting precision, as seen from the lower RMSE value of 93.2045. The results provide valuable insights into weather prediction methodologies, benefiting authorities and the public.

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Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches

Cited by 30 publications

References 35 publications

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Utilizing the Sarima Model and Support Vector Regression to Forecast Monthly Rainfall in Bandung City

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