GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India

Saikh, Nur Islam; Mondal, Prolay

doi:10.1016/j.nhres.2023.05.004

Cited by 24 publications

(8 citation statements)

References 121 publications

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“…The basin's topographical heterogeneity, comprising both mountainous and plain terrains, is considered, with particular attention paid to the flood-vulnerabilities of the Brahmaputra Valley in India and Bangladesh during monsoon seasons. Pham et al (2021), Saikh and Mondal (2023) Pham et al (2021), and Saikh and Mondal (2023 determined that, despite the models' differing performance in dividing the research area into various levels of flood susceptibility, all the models agreed that the low-lying parts near the rivers are the most flood-prone portions of the study area. The same conclusions were arrived at during the present study.…”

Section: Discussionmentioning

confidence: 96%

“…The RF algorithm serves as an ensemble learning method and operates by utilizing a multitude of straightforward decision trees for both classification and regression tasks (Saikh and Mondal 2023). It was originally developed based on the random-subspace approach for random-decision forests (Ho 1995).…”

Section: Random Forest (Rf) Algorithmmentioning

confidence: 99%

“…The algorithm tends to underperform when target classes are non-linearly separable or when the dataset is exceptionally large and noisy. In the current study, the RBF kernel was selected for the SVM model due to its proven efficacy in multiple research settings (Pourghasemi et al 2013;Saikh and Mondal 2023). By integrating these machine learning algorithms, this study aims to offer an empirically substantiated, technologically robust framework for flood susceptibility mapping in the Brahmaputra Basin.…”

Section: Support Vector Machine (Svm) Algorithmmentioning

confidence: 99%

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Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

Debnath,

Sahariah,

Mazumdar

et al. 2023

Earth Syst Environ

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Floods represent a significant threat to human life, property, and agriculture, especially in low-lying floodplains. This study assesses flood susceptibility in the Brahmaputra River basin, which spans China, India, Bhutan, and Bangladesh—an area notorious for frequent flooding due to the saturation of river water intake capacity. We developed and evaluated several innovative models for predicting flood susceptibility by employing Multi-Criteria Decision Making (MCDM) and Machine Learning (ML) techniques. The models showed robust performance, evidenced by Area Under the Receiver Operating Characteristic Curve (AUC-ROC) scores exceeding 70% and Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) scores below 30%. Our findings indicate that approximately one-third of the studied region is categorized as moderately to highly flood-prone, while over 40% is classified as low to very low flood-risk areas. Specific regions with high to very high flood susceptibility include Dhemaji, Dibrugarh, Lakhimpur, Majuli, Darrang, Nalbari, Barpeta, Bongaigaon, and Dhubri districts in Assam; Coochbihar and Jalpaiguri districts in West Bengal; and Kurigram, Gaibandha, Bogra, Sirajganj, Pabna, Jamalpur, and Manikganj districts in Bangladesh. Owing to their strong performance and the suitability of the training datasets, we recommend the application of the developed MCDM techniques and ML algorithms in geographically similar areas. This study holds significant implications for policymakers, regional administrators, environmentalists, and engineers by informing flood management and prevention strategies, serving as a climate change adaptive response within the Brahmaputra River basin.

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Section: Discussionmentioning

confidence: 96%

Section: Random Forest (Rf) Algorithmmentioning

confidence: 99%

Section: Support Vector Machine (Svm) Algorithmmentioning

confidence: 99%

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Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

Debnath,

Sahariah,

Mazumdar

et al. 2023

Earth Syst Environ

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show abstract

“…Seventy-eight per cent of the world's population live in rural areas and depend mainly on farming for their livelihood, especially in developing countries of Asia and Africa (Paracchini et al, 2022). According to the Food and Agriculture Organization of the United Nations, over one billion of the labor force of the world that is engaged directly and indirectly with this sector is one-third of the total workforce of the world (Khan et al, 2022;Saikh et al, 2023). Generally, the population comprising the labor force associated with agriculture is divided into different categories by the economists like wage laborers, sharecroppers, peasants and farmers having subsistence farming (Stratton et al, 2021& Sumberg et al, 2022.…”

Section: Introductionmentioning

confidence: 99%

Socio-Economic Problems of Agricultural Laborers in Punjab (Pakistan)

Jamshed,

Afzal,

Arshad

2024

J. Asian Dev. Studies

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The development of the agriculture sector is one of the most imperative factors for fulfilling basic human needs, alleviating poverty, boosting shared prosperity, and becoming a significant contributor to national development for most developing countries. Over one billion of the world's labour force is engaged directly and indirectly in this sector, one-third of the world's workforce. Unfortunately, the laborers working in the agriculture sector face multidimensional socioeconomic problems such as poor livelihood conditions, meagre employment circumstances, long working hours, fewer rewards, unavailability of basic safety facilities, remuneration and pitiable nutrition, among others. To understand the contributing factors responsible for socioeconomic problems, this study was conducted in three divisions of Punjab province: Gujranwala, Faisalabad, and Dera Ghazi Khan. For this purpose, a multistage simple random sampling procedure was applied to approach the participants. The interview schedule was used as a tool for data collection and validated through pilot testing. The study's findings revealed that the laborers’ employment conditions could have been more conducive while agricultural laborers were receiving lesser wages. The agricultural laborers have to face many health hazards. In contrast, the majority of the laborers were exploited only due to their illiteracy and ignorance about Government policies and laws related to them. Many participants reported chronic socioeconomic problems significantly associated with their working environment, amenities and available facilities.

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“…Empirical evidence highlighted the various occupational and environmental health hazards incidents, which provide intensive understanding regarding the issues (Pradhan et al, 2022). There are a variety of incidents caused by major occupational and workplace mismanagements, along with major or minor injuries (Saikh et al, 2023). Every year, a significant number of deaths or injuries that lead towards later death are reported, with the rest to occupational and workplace incidents that have long-lasting impacts on agriculture laborer and their families.…”

Section: Introductionmentioning

confidence: 99%

Occupational and Environmental Health Hazards among Agriculture Laborers: An Evidence-Based Study from Punjab, Pakistan

Jamshed,

Arshad,

Afzal

et al. 2024

QJSSH

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Laborers from the agriculture sector have often been victims rather than beneficiaries of the green revolution, technological development, and globalization trends that characterized the 20th century. In recent decades, the laborers working in the agriculture sector have been half of the world’s labor force, with an estimated number of 1.3 billion worldwide, and a majority of them found in developing countries. Adequate balance between agricultural growth and protection from occupational & environmental health hazards is very crucial for the future of the world’s food production and for the sustainability of the sector. The study was conducted in three divisions of Punjab, and primary data was collected through multistage simple random sampling techniques. The interview schedule was used as a tool for data collection. The results revealed that the occupational and environmental circumstances of the laborers were not conducive towards their health and well-being. The agricultural laborers have to face many health hazards, whereas the majority of the laborers were exploited only due to their illiteracy and ignorance about Government policies and laws related to them. A huge proportion of the participants reported chronic health, malnutrition, and socio-economic problems significantly associated with their working environment, amenities and available facilities.

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GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India

Cited by 24 publications

References 121 publications

Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

Socio-Economic Problems of Agricultural Laborers in Punjab (Pakistan)

Occupational and Environmental Health Hazards among Agriculture Laborers: An Evidence-Based Study from Punjab, Pakistan

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