Separation and attribution of impacts of changes in land use and climate on hydrological processes

Polong, Francis; Deng, Khidir; Pham, Quoc Bao; Linh, Nguyễn Thị Thùy; Abba, S.I.; Ahmed, Ali Najah; Anh, Duong Tran; Khedher, Khaled Mohamed; El‐Shafie, Ahmed

doi:10.1007/s00704-022-04351-7

Cited by 7 publications

(2 citation statements)

References 57 publications

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“…Modeling terrestrial water content is crucial for pursuing the sustainable development goals issued by the 2030 agenda of the United Nations, especially for the sixth goal: Ensure availability and sustainable management of water and sanitation for all. Moreover, climate and land-use change could put more stress on water resources and hydro-power generation, especially if they act together [6][7][8]. Extreme hydrological events (i.e., droughts and floods) have a severe impact on a region, making environmental resource policies very relevant to the development of a country.…”

Section: The Motivations Of the Case Studymentioning

confidence: 99%

Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire

Ferraris,

Meo,

Pinardi

et al. 2023

Sensors

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Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to take valid and coordinated countermeasures to combat climate change. This article discusses how artificial intelligence in agriculture can help to reduce costs, especially in developing countries such as Côte d’Ivoire, employing only low-cost or open-source tools, from hardware to software and open data. We developed machine learning models for two tasks: the first is improving agricultural farming cultivation, and the second is water management. For the first task, we used deep neural networks (YOLOv5m) to detect healthy plants and pods of cocoa and damaged ones only using mobile phone images. The results confirm it is possible to distinguish well the healthy from damaged ones. For actions at a larger scale, the second task proposes the analysis of remote sensors, coming from the GRACE NASA Mission and ERA5, produced by the Copernicus climate change service. A new deep neural network architecture (CIWA-net) is proposed with a U-Net-like architecture, aiming to forecast the total water storage anomalies. The model quality is compared to a vanilla convolutional neural network.

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Section: The Motivations Of the Case Studymentioning

confidence: 99%

Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire

Ferraris,

Meo,

Pinardi

et al. 2023

Sensors

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“…Land use/land cover change (LULCC) involves the inter-conversions of the land cover types by humans [1] [2] [3] influenced by biophysical and human factors.…”

Section: Introductionmentioning

confidence: 99%

Modelling Land Use/Land Cover Change of River Rwizi Catchment, South-Western Uganda Using GIS and Markov Chain Model

Muhangane,

Andama

2024

JWARP

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Analysis of catchment Land use/Land cover (LULC) change is a vital tool in ensuring sustainable catchment management. The study analyzed land use/ land cover changes in the Rwizi catchment, south western Uganda from 1989-2019 and projected the trend by 2040. Landsat images, field observations, key informant interviews and focus group discussions were used to collect data. Changes in cropland, forestland, built up area, grazing land, wetland and open water bodies were analyzed in ArcGIS version 10.2.2 and ERDAS IMAGINE 14 software and a Markov chain model. All the LULC classes increased in area except grazing land. Forest land and builtup area between 2009-2019 increased by 370.03% and 229.53% respectively. Projections revealed an increase in forest land and builtup area by 2030 and only built up area by 2040. LULCC in the catchment results from population pressure, reduced soil fertility and high value of agricultural products.

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