Artificial neural network for flood susceptibility mapping in Bangladesh

Rudra, Rhyme Rubayet; Sarkar, Showmitra Kumar

doi:10.1016/j.heliyon.2023.e16459

Cited by 19 publications

(6 citation statements)

References 51 publications

(91 reference statements)

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“…80% training data has been used to create the three different map. Previous studies related to machine learning models have used the same type of procedure 70 . Kalarola upazila has minimal salinity in all three models.…”

Section: Discussionmentioning

confidence: 99%

“…Equation ( 2 ) is used to calculate the sigmoid function. Multilayer perceptron result in complete neuron connectivity—each neuron in the layer is connected to all neurons in the preceding (following) layer 70 . Complex connections between input data and output predictions can be learned by ANNs: The complicated phenomena of soil salinity is regulated by a number of variables, such as climatic conditions, soil properties, and agricultural activities.…”

Section: Methodsmentioning

confidence: 99%

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Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh

Sarkar,

Rudra,

Sohan

et al. 2023

Sci Rep

Self Cite

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Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh

Sarkar,

Rudra,

Sohan

et al. 2023

Sci Rep

Self Cite

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show abstract

“…Every neuron in a multilayer perceptron network functions similarly to a perceptron. A sigmoid function is the most popular activation function for neurons in multilayer perceptron, which are differentiable continuous functions 65 , 66 . …”

Section: Methodsmentioning

confidence: 99%

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Sarkar,

Rudra,

Talukdar

et al. 2024

Sci Rep

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The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

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“…This enables evaluation of landscapes dynamics and assessment of environmental properties [91], as well as the application of advanced methods of segmentation [92], feature extraction and classification [93] for data analysis. Further development of DL methods aims at achieving high-precision image processing using information obtained from spectral reflectances of the land cover types detected in images, as well as the use of complex programming algorithms, e.g., NNs [94].…”

Section: Deep Learningmentioning

confidence: 99%

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Lemenkova

2024

Water

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Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta.

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Artificial neural network for flood susceptibility mapping in Bangladesh

Cited by 19 publications

References 51 publications

Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh

Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

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