Spatial prediction of flood-susceptible zones in the Ourika watershed of Morocco using machine learning algorithms

Meliho, Modeste; Khattabi, Abdellatif; Zejli, Driss; Orlando, Collins Ashianga

doi:10.1108/aci-09-2021-0264

Cited by 9 publications

(2 citation statements)

References 50 publications

(54 reference statements)

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“…Soil and vegetation carbon sequestration plays a pivotal role in mitigating climate change by offsetting the impacts of greenhouse gases (GHGs) (Janzen 2004, Meliho et al 2023. Globally, soil holds more than 2300 billion tonnes of organic carbon, making it the largest terrestrial reservoir of organic carbon (Stockmann et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Carbon pool and respiration of rhizosphere soils of different mangrove plant species in bangladesh sundarbans

Alam,

Yeasin,

Ahmed

2024

Bangladesh J. Bot.

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Bangladesh Sundarbans like other mangrove ecosystems are vital carbon reservoirs in the global carbon cycle. Soil respiration, a key carbon flux, is closely linked to climate change. Despite extensive research on the Sundarbans, a gap exists in studying rhizosphere soil carbon pool (SOC) and respiration (Rs), which is crucial for understanding its role in global climate dynamics, especially the local climate. This study investigated SOC pools and Rs rates of oligohaline, mesohaline, and polyhaline zones of the Bangladesh Sundarban Mangrove Forests (SMF). The oligohaline zone exhibited the highest average SOC content (11.26 ± 5.52 t/ha), followed by the mesohaline zone (9.91 ± 3.09 t/ha) and the polyhaline zone (9.86 ± 4.16 t/ha). The Rs rate was comparatively higher in the mesohaline zone (28.19 ± 5.02 mg CO2/g soil), followed by the polyhaline zone (27.81 ± 4.38 mg CO2/g soil), and the oligohaline zone (27.63 ± 4.16 mg CO2/g soil) though the differences were not significant. Further analyses explored the influences of plant species on SOC and Rs. While rhizosphere soil of distinct plant species displayed varying SOC values, Rs did not exhibit significant differences among different plant species, and no significant relation was observed between Rs and SOC values. Mangroves were noted to store substantial amounts of organic carbon in their soils, yet they released relatively less carbon dioxide (CO2) through soil respiration compared to other tropical forests. This unique characteristic underscores the critical role of mangroves in global climate change dynamics. Conclusively, this study offers insightful information about the carbon dynamics of the Bangladesh SMF, emphasizing the significance of mangroves as carbon reservoirs with the potential to influence climate change adaptation strategies. Bangladesh J. Bot. 53(1): 131-140, 2024 (March)

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Section: Introductionmentioning

confidence: 99%

Carbon pool and respiration of rhizosphere soils of different mangrove plant species in bangladesh sundarbans

Alam,

Yeasin,

Ahmed

2024

Bangladesh J. Bot.

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“…Furthermore, stochastic models adapt to the non-linearity of hydrological processes and address uncertainties in parameter estimations. [11,12]. On the other hand, stochastic models introduce various techniques for flood estimation, ranging from simple regression of discharge to detailed modeling of hydrological processes.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network

Dabire,

Ezin,

Firmin

2024

Preprint

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The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of lake Nokoue in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Lake Nokoue, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

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Flood subsidence susceptibility mapping using persistent scatterer SAR interferometry technique coupled with novel metaheuristic approaches from Jeddah, Saudi Arabia

Abba,

Al-Areeq,

Ghaleb

et al. 2024

Neural Comput & Applic

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Spatial prediction of flood-susceptible zones in the Ourika watershed of Morocco using machine learning algorithms

Cited by 9 publications

References 50 publications

Carbon pool and respiration of rhizosphere soils of different mangrove plant species in bangladesh sundarbans

Carbon pool and respiration of rhizosphere soils of different mangrove plant species in bangladesh sundarbans

Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network

Flood subsidence susceptibility mapping using persistent scatterer SAR interferometry technique coupled with novel metaheuristic approaches from Jeddah, Saudi Arabia

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