Your search found 3 records
1 Mia, Md. U.; Rahman, M.; Elbeltagi, A.; Abdullah-Al-Mahbub, Md.; Sharma, G.; Islam, H. M. T.; Pal, S. C.; Costache, R.; Towfiqul Islam, A. R. Md.; Islam, Md. M.; Chen, N.; Alam, E.; Washakh, R. M. A. 2022. Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology. Geocarto International, 30p. (Online first) [doi: https://doi.org/10.1080/10106049.2022.2112982]
Flooding ; Risk assessment ; Disaster risk management ; Machine learning ; Blockchain technology ; Neural networks ; Sustainable development ; Floodplains ; Rain ; Forecasting ; Datasets ; Mapping ; Normalized difference vegetation index ; Models / Bangladesh / Brahmaputra River / Jamalpur / Gaibandha / Kurigram / Bogra
(Location: IWMI HQ Call no: e-copy only Record No: H051339)
https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2112982
https://vlibrary.iwmi.org/pdf/H051339.pdf
https://vlibrary.iwmi.org/pdf/H051339.pdf
(5.41 MB) (5.41 MB)
The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events.
2 Brouziyne, Youssef; El Bilali, A.; Epule, T. E.; Ongoma, V.; Elbeltagi, A.; Hallam, J.; Moudden, F.; Al-Zubi, Maha; Vadez, V.; McDonnell, Rachael. 2023. Towards lower greenhouse gas emissions agriculture in North Africa through climate-smart agriculture: a systematic review. Climate, 11(7):139. [doi: https://doi.org/10.3390/cli11070139]
Climate-smart agriculture ; Greenhouse gas emissions ; Emission reduction ; Climate change mitigation ; Carbon sequestration ; Agricultural practices ; Conservation tillage ; Soil organic carbon ; Systematic reviews / North Africa / Egypt / Libya / Tunisia / Algeria / Morocco
(Location: IWMI HQ Call no: e-copy onl Record No: H052079)
https://www.mdpi.com/2225-1154/11/7/139/pdf?version=1688377462
https://vlibrary.iwmi.org/pdf/H052079.pdf
https://vlibrary.iwmi.org/pdf/H052079.pdf
(1.07 MB) (1.07 MB)
North Africa (NA) is supposed to lower emissions in its agriculture to honor climate action commitments and to impulse sustainable development across Africa. Agriculture in North Africa has many assets and challenges that make it fit to use the tools of Climate-Smart Agriculture (CSA) for mitigation purposes. This study represents a first attempt to understand if CSA practices are sufficiently established in NA to contribute to reducing agriculture emissions. A PRISMA-inspired systematic review was carried out on an initial 147 studies retrieved from Scopus, Google Scholar, and the Web of Science databases, as well as from gray literature. 11 studies were included in the final analysis since they report the mitigation and co-benefits of CSA-based practices within NA. A bias risk was identified around the optimal inclusion of studies produced in French, and a specific plan was set for its minimization. Synthesis results revealed that most studies focused either on improving soil quality (nine studies) or managing enteric fermentation (two studies). The review revealed a poor establishment of the CSA framework in the region, especially in sequestering GHG emissions. A set of recommendations has been formulated to address the identified gaps from research orientations and organizational perspectives and empower the CSA as an ally for mitigation in north African agriculture.
3 Baghel, S.; Tripathi, M. P.; Khalkho, D.; Al-Ansari, N.; Kumar, A.; Elbeltagi, A.. 2023. Delineation of suitable sites for groundwater recharge based on groundwater potential with RS, GIS, and AHP approach for Mand catchment of Mahanadi Basin. Scientific Reports, 13:9860. [doi: https://doi.org/10.1038/s41598-023-36897-5]
Groundwater potential ; Groundwater recharge ; Remote sensing ; Geographical information systems ; Groundwater table ; Drainage ; Land use ; Land cover ; Digital elevation models ; Infiltration ; Soil texture ; Rainfall ; Farmland ; Curvature / India / Chhattisgarh / Mahanadi Basin
(Location: IWMI HQ Call no: e-copy only Record No: H052140)
(3.87 MB) (3.87 MB)
Groundwater management requires a systematic approach since it is crucial to the long-term viability of livelihoods and regional economies all over the world. There is insufficient groundwater management and difficulties in storage plans as a result of increased population, fast urbanisation, and climate change, as well as unpredictability in rainfall frequency and intensity. Groundwater exploration using remote sensing (RS) data and geographic information system (GIS) has become a breakthrough in groundwater research, assisting in the assessment, monitoring, and conservation of groundwater resources. The study region is the Mand catchment of the Mahanadi basin, covering 5332.07 km2 and is located between 21°42'15.525"N and 23°4'19.746"N latitude and 82°50'54.503"E and 83°36'1.295"E longitude in Chhattisgarh, India. The research comprises the generation of thematic maps, delineation of groundwater potential zones and the recommendation of structures for efficiently and successfully recharging groundwater utilising RS and GIS. Groundwater Potential Zones (GPZs) were identified with nine thematic layers using RS, GIS, and the Multi-Criteria Decision Analysis (MCDA) method. Satty's Analytic Hierarchy Process (AHP) was used to rank the nine parameters that were chosen. The generated GPZs map indicated regions with very low, low to medium, medium to high, and very high groundwater potential encompassing 962.44 km2, 2019.92 km2, 969.19 km2, and 1380.42 km2 of the study region, respectively. The GPZs map was found to be very accurate when compared with the groundwater fluctuation map, and it is used to manage groundwater resources in the Mand catchment. The runoff of the study area can be accommodated by the computing subsurface storage capacity, which will raise groundwater levels in the low and low to medium GPZs. According to the study results, various groundwater recharge structures such as farm ponds, check dams and percolation tanks were suggested in appropriate locations of the Mand catchment to boost groundwater conditions and meet the shortage of water resources in agriculture and domestic use. This study demonstrates that the integration of GIS can provide an efficient and effective platform for convergent analysis of various data sets for groundwater management and planning.
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