scholarly journals Quantification of Groundwater Exploitation and Assessment of Water Quality Risk Perception in the Dar Es Salaam Quaternary Aquifer, Tanzania

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2552
Author(s):  
Kassim Mussa ◽  
Ibrahimu Mjemah ◽  
Kristine Walraevens
Keyword(s):  
Water Quality ◽  
Risk Perception ◽  
Time Series Data ◽  
Household Survey ◽  
Dar Es Salaam ◽  
Series Data ◽  
Groundwater Exploitation ◽  
Quaternary Aquifer ◽  
Quality Risk ◽  
Household Heads

This study quantified groundwater exploitation and assessed water quality risk perception in the Dar es Salaam quaternary aquifer through a socio-economic survey. Annual total groundwater exploitation was estimated, using the daily per capita consumption of groundwater derived from the household survey. A logistic regression analysis was performed to ascertain the influence of sex, marital status, education level, employment, income, and cost of water on groundwater quality risk perception. It was revealed that most residents of Dar es Salaam use groundwater as a main source of water supply. The results of this study further showed that 78% consider groundwater as a reliable source. Averting strategies for insufficient quantity of groundwater consist of minimizing less necessary water uses, while for poor quality, buying bottled water and water treatment by boiling and using chemicals. The chance for water quality risk perception is 0.205 times greater for married than unmarried household heads, and it is 623 times higher for employed versus unemployed household heads. To get an overall view of the importance of groundwater for domestic needs in Dar es Salaam it is imperative to combine a time series data of groundwater and surface water exploitation.

scholarly journals Spatial Pattern of Agricultural Productivity Trends in Malawi

2020 ◽  
Vol 12 (4) ◽  
pp. 1313
Author(s):  
Leah M. Mungai ◽  
Joseph P. Messina ◽  
Sieglinde Snapp
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Agricultural Productivity ◽  
Household Survey ◽  
Maize Yield ◽  
Series Data ◽  
Maize Crop

This study aims to assess spatial patterns of Malawian agricultural productivity trends to elucidate the influence of weather and edaphic properties on Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) seasonal time series data over a decade (2006–2017). Spatially-located positive trends in the time series that can’t otherwise be accounted for are considered as evidence of farmer management and agricultural intensification. A second set of data provides further insights, using spatial distribution of farmer reported maize yield, inorganic and organic inputs use, and farmer reported soil quality information from the Malawi Integrated Household Survey (IHS3) and (IHS4), implemented between 2010–2011 and 2016–2017, respectively. Overall, remote-sensing identified areas of intensifying agriculture as not fully explained by biophysical drivers. Further, productivity trends for maize crop across Malawi show a decreasing trend over a decade (2006–2017). This is consistent with survey data, as national farmer reported yields showed low yields across Malawi, where 61% (2010–11) and 69% (2016–17) reported yields as being less than 1000 Kilograms/Hectare. Yields were markedly low in the southern region of Malawi, similar to remote sensing observations. Our generalized models provide contextual information for stakeholders on sustainability of productivity and can assist in targeting resources in needed areas. More in-depth research would improve detection of drivers of agricultural variability.

A Chaotic Prediction Method to Time Series Data

2010 ◽  
Vol 113-116 ◽  
pp. 1367-1370 ◽  
Author(s):  
Bin Sheng Liu ◽  
Ying Wang ◽  
Xue Ping Hu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Time Series ◽  
Drinking Water ◽  
Time Series Data ◽  
Prediction Method ◽  
Drinking Water Quality ◽  
Series Data ◽  
Gray Model ◽  
Forecast Method

There are many ways to predict drinking water quality such as neural network, gray model, ARIMA. But the prediction precise is need to improve. This paper proposes a new forecast method according the characteristic of drinking water quality and the evidence showed that the prediction is effectively. So it is able to being used in actual prediction.

scholarly journals Land Use Land Cover Change and its Pathways in Sidin VDC, Panchthar District, Nepal

2018 ◽  
Vol 11 ◽  
pp. 77-94 ◽  
Author(s):  
Prem Sagar Chapagain ◽  
Mohan Kumar Rai ◽  
Basanta Paudel
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Agricultural Land ◽  
Time Series Data ◽  
Household Survey ◽  
Human Interaction ◽  
Series Data ◽  
Land Use Land Cover ◽  
Important Indicator

Land use/land cover situation is an important indicator of human interaction with environment. It reflects both environmental situation and the livelihood strategies of the people in space over time. This paper has attempted to study the land use/ land cover change of Sidin VDC, in the Koshi River basin in Nepal, based on maps and Remote sensing imageries (RS) data and household survey using structured questionnaires, focus group discussion and key informant interview. The study has focused on analysis the trend and pathways of land use change by dividing the study area into three elevation zones – upper, middle and lower. The time series data analysis from 1994-2004-2014 show major changes in forest and agricultural land. The dominant pathways of change is from forest to agriculture and forest to shrub during 1994-2004 and agriculture to forest during 2004-2014. The development of community forest, labor migration and labor shortage are found the major causes of land use change.The Geographical Journal of NepalVol. 11: 77-94, 2018

scholarly journals Explainable AI Framework for Multivariate Hydrochemical Time Series

2021 ◽  
Vol 3 (1) ◽  
pp. 170-204
Author(s):  
Michael C. Thrun ◽  
Alfred Ultsch ◽  
Lutz Breuer
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Time Series Data ◽  
Distance Measure ◽  
Multivariate Time Series ◽  
Environmental Parameters ◽  
Series Data ◽  
Topographic Map ◽  
Domain Experts ◽  
Explainable Ai

The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series. The XAI provides explanations that are interpretable by domain experts. In three steps, it combines a data-driven choice of a distance measure with supervised decision trees guided by projection-based clustering. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The framework, called DDS-XAI, does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two state of the art XAIs called eUD3.5 and iterative mistake minimization (IMM) were unable to provide meaningful and relevant explanations from the three multivariate time series data. The DDS-XAI framework can be swiftly applied to new data. Open-source code in R for all steps of the XAI framework is provided and the steps are structured application-oriented.

scholarly journals Explainable AI Framework for Multivariate Hydrochemical Time Series

2020 ◽  
Author(s):  
Michael Thrun ◽  
Alfred Ultsch ◽  
Lutz Breuer
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Time Series Data ◽  
Distance Measure ◽  
Multivariate Time Series ◽  
Environmental Parameters ◽  
Series Data ◽  
Domain Expert ◽  
Topographic Map ◽  
Explainable Ai

The understanding of water quality and its underlying processes is important for the protection of aquatic environments enabling the rare opportunity of access to a domain expert. Hence, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series resulting in explanations that are interpretable by a domain expert. The XAI combines in three steps a data-driven choice of a distance measure with explainable cluster analysis through supervised decision trees. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The XAI does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two comparable decision-based XAIs were unable to provide meaningful and relevant explanations from the multivariate time series data. Open-source code in R for the three steps of the XAI framework is provided.

Impact of the As Samra wastewater treatment plant upgrade on the water quality (COD, electrical conductivity, TP, TN) of the Zarqa River

2013 ◽  
Vol 67 (7) ◽  
pp. 1455-1464 ◽  
Author(s):  
A. Al-Omari ◽  
Z. Al-houri ◽  
R. Al-Weshah
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Time Series ◽  
Wastewater Treatment ◽  
Time Series Data ◽  
Wastewater Reuse ◽  
Treatment Plant ◽  
Series Data ◽  
Before And After ◽  
Zarqa River

The impact of the As Samra wastewater treatment plant upgrade on the quality of the Zarqa River (ZR) water was investigated. Time series data that extend from October 2005 until December 2009 obtained by a state-of-the-art telemetric monitoring system were analyzed at two monitoring stations located 4 to 5 km downstream of the As Samra effluent confluence with the Zarqa River and about 25 km further downstream. Time series data that represent the ZR water quality before and after the As Samra upgrade were analyzed for chemical oxygen demand (COD), electrical conductivity (EC), total phosphorus (TP) and total nitrogen (TN). The means of the monitored parameters, before and after the As Samra upgrade, showed that the reductions in the COD, TP and TN were statistically significant, while no reduction in the EC was observed. Comparing the selected parameters with the Jordanian standards for reclaimed wastewater reuse in irrigation and with the Ayers & Westcot guidelines for interpretation of water quality for irrigation showed that the ZR water has improved towards meeting the required standards and guidelines for treated wastewater reuse in irrigation.

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