“Not seen, not considered”: mapping local perception of environmental risks in the Plain of Mornag and Jebel Ressass (Tunisia)

Saqalli, Mehdi; Hamrita, Abdelkarim; Maestripieri, Nicolas; Boussetta, Amira; Rejeb, Hichem; Olmo, Rafael Mata; Kassouk, Zeineb; Belem, Mahamadou; Sáenz, Melio; Mouri, Hassan

doi:10.1007/s41207-020-00166-y

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Drainage density is crucial for flood risk management, as drainage density discloses the soil's composition and geotechnical properties. It is one of the most important criteria in defining a region's susceptibility to floods [91]. Both models achieved AUC values higher than 0.84 in the ROC investigation, demonstrating their effective performance in predicting flood susceptibility.…”

Section: Discussionmentioning

confidence: 87%

Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Al-Aizari,

Alzahrani,

AlThuwaynee

et al. 2024

Remote Sensing

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Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy of the produced maps. Challenges such as limited data, uncertainty due to confidence bounds, and the overfitting problem are critical areas for improving accurate models. We focus on the uncertainty in susceptibility mapping, mainly when there is a significant variation in the predictive relevance of the predictor factors. It is also noted that the receiver operating characteristic (ROC) curve may not accurately depict the sensitivity of the resulting susceptibility map to overfitting. Therefore, reducing the overfitting problem was targeted to increase accuracy and improve processing time in flood prediction. This study created a spatial repository to test the models, containing data from historical flooding and twelve topographic and geo-environmental flood conditioning variables. Then, we applied random forest (RF) and extreme gradient boosting (XGB) algorithms to map flood susceptibility, incorporating a variable drop-off in the empirical loop function. The results showed that the drop-off loop function was a crucial method to resolve the model uncertainty associated with the conditioning factors of the susceptibility modelling and methods. The results showed that approximately 8.42% to 9.89% of Marib City and 9.93% to 15.69% of Shibam City areas were highly vulnerable to floods. Furthermore, this study significantly contributes to worldwide endeavors focused on reducing the hazards linked to natural disasters. The approaches used in this study can offer valuable insights and strategies for reducing natural disaster risks, particularly in Yemen.

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Section: Discussionmentioning

confidence: 87%

Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Al-Aizari,

Alzahrani,

AlThuwaynee

et al. 2024

Remote Sensing

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“…The mountains dominate the Mornag plain. Past study by Saqalli et al (2020) revealed that olive trees are cropped intensively and threatened by the expansion of the nearby suburbs of Tunis due to the gap between new peri urban consumption practices in suburban Tunis and rural municipality budgets. Other agriculture-related threat highlights the gap between the vision of a preserved rural area and the agroindustrial reality.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Farms’ Sustainability According to Land Tenure in Mornag, Tunisia

Gara

Aounallah

Ajabi

2020

Caraka Tani J. Sustain. Agric.

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<p>In Tunisia the main objective of sustainable agriculture is to improve agricultural systems by creating more wealth and meeting the needs of the population without compromising those of future generations. It turned out that confusion and lack of clarity of ownership status of agricultural land would lead to instability in agricultural sustainability by having a negative impact on social and economic prosperity and environment preservation. Within this environmental concern, the current research constitutes an assessment of the level of sustainability of different farms in Tunisia where problems of land ownership still exist. The current research is based on the Farm Sustainability Indicators method (standing for: <em>‘Indicateurs de durabilité des exploitations agricoles </em>or IDEA). This case study employed IDEA method which provides operational content to compare the sustainability concept at the farm level, based on the factor of ‘land tenure status’. In Tunisia, the lack of clear land ownership affects the output of agriculture that is why it is valuable to assess the impact of land properties status on farms’ agricultural sustainability. Hence, we distinguished two groups of farms: Private Farms (PF) (inherited from father to son) and Farming Enhancement Corporation (FEC) (state’s lands leased by farmers for many years). The results showed that land tenure has impact on farms sustainability. PF are more sustainable on both agro-ecological scale (49.2) and socio-territorial scale (48); compared to FEC (agro-ecological: 44.5 and socio-territorial: 46). PF favor integrated systems and the production of field crops and fodder crops and they attach greater importance to their employees’ training.</p>

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Spatial distribution and ecological risk assessment of potentially toxic elements in agricultural soils, stream sediments, and plants around Lakhouat mine (northwestern Tunisia)

et al. 2021

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“Not seen, not considered”: mapping local perception of environmental risks in the Plain of Mornag and Jebel Ressass (Tunisia)

Cited by 5 publications

References 27 publications

Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Evaluation of Farms’ Sustainability According to Land Tenure in Mornag, Tunisia

Spatial distribution and ecological risk assessment of potentially toxic elements in agricultural soils, stream sediments, and plants around Lakhouat mine (northwestern Tunisia)

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