Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning

Abstract

Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine learning methods and one parametric model. This performance can be considered satisfactory as classical empirical relationships between Chla and phosphorus may not hold for tropical reservoirs. Water volume and the mix-layer depth are inversely related to Chla, while mean surface temperature, water level, and surface solar radiation have direct relationships with Chla. These findings provide insights on how seasonal climate prediction and reservoir operation might influence water quality in regions supplied by superficial reservoirs.

Publication
Environmental Science and Pollution Research
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title: “Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil” authors:

  • Nunes Carvalho, T. M.
  • Souza Filho, F. D. A.
  • Costa Porto, V. author_notes:

date: “2021” doi: ""

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abstract: Despite recent efforts to apply machine learning (ML) for water demand modeling, overcoming the black-box nature of these techniques to extract practical information remains a challenge, especially in developing countries. This study integrated random forest (RF), self-organizing map (SOM), and artificial neural network (ANN) techniques to assess water demand patterns and to develop a predictive model for the city of Fortaleza, Brazil. We performed the analysis at two spatial scales, with different level of information: census tracts (CTs) at the fine scale, and census blocks (CBs) at the coarse scale. At the CB scale, demand was modeled with socioeconomic, demographic, and household characteristics. The RF technique was applied to rank these variables, and the most relevant were used to cluster census blocks with SOMs. RFs and ANNs were used in an iterative approach to define the input variables for the predictive model with minimum redundancy. At the CT scale, demand was modeled using HDI and per capita income. Variables which assess the education level and economic aspects of households demonstrated a direct relationship with water demand. The analysis at the coarse scale provided more insight into the relationship between the variables; however, the predictive model performed better at the fine scale. This study demonstrates how data-driven models can be helpful for water management, especially in environments with strong socioeconomic inequalities, where urban planning decisions should be integrated and inclusive.

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url_pdf: [https://link.springer.com/article/10.1007/s11356-022-21168-z]

url_code: ‘https://github.com/HugoBlox/hugo-blox-builder'

url_dataset: ‘https://github.com/taiscarvalho/chla-prediction-ce' url_poster: ’’ url_project: ’’ url_slides: ’' url_source: ’' url_video: ''

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