scholarly journals Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran

2010 ◽  
Vol 13 (4) ◽  
pp. 867-876 ◽  
Author(s):  
Amir Jalalkamali ◽  
Hossein Sedghi ◽  
Mohammad Manshouri
Keyword(s):  
Arid Regions ◽  
Groundwater Resources ◽  
Good Choice ◽  
Groundwater Levels ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Groundwater Level Prediction

The prediction of groundwater levels in a well has immense importance in the management of groundwater resources, especially in arid regions. This paper investigates the abilities of neuro-fuzzy (NF) and artificial neural network (ANN) techniques to predict the groundwater levels. Two different NF and ANN models comprise various combinations of monthly variablities, that is, air temperature, rainfall and groundwater levels in neighboring wells. The result suggests that the NF and ANN techniques are a good choice for the prediction of groundwater levels in individual wells. Also based on comparisons, it is found that the NF computing techniques have better performance than the ANN models in this case.

scholarly journals Shallow groundwater in sub-Saharan Africa: neglected opportunity for sustainable intensification of small-scale agriculture?

2016 ◽  
Author(s):  
John Gowing ◽  
Geoff Parkin ◽  
Nathan Forsythe ◽  
David Walker ◽  
Alemseged Tamiru Haile ◽  
...  
Keyword(s):  
Groundwater Resources ◽  
Climatic Variability ◽  
Shallow Groundwater ◽  
Sustainable Intensification ◽  
Sub Saharan Africa ◽  
Small Scale ◽  
Groundwater Levels ◽  
Sub Saharan ◽  
Small Scale Irrigation

Abstract. There is a need for an evidence-based approach to identify how best to support development of groundwater for small scale irrigation in sub-Saharan Africa (SSA). We argue that it is important to focus this effort on shallow groundwater resources which are most likely to be used by poor rural communities in SSA. However, it is important to consider constraints, since shallow groundwater resources are likely to be vulnerable to over-exploitation and climatic variability. We examine here the opportunities and constraints and draw upon evidence from Ethiopia. We present a methodology for assessing and interpreting available shallow groundwater resources and argue that participatory monitoring of local water resources is desirable and feasible. We consider possib le models for developing distributed small-scale irrigation and assess its technical feasibility. Because of power limits on water lifting and also because of available technology for well construction, groundwater at depths of 50 m or 60 m cannot be regarded as easily accessible for small-scale irrigation. We therefore adopt a working definition of shallow groundwater as < 20 m depth. This detailed case study in the Dangila woreda in Ethiopia explores the feasibility of exploiting shallow groundwater for small-scale irrigation over a range of rainfall conditions. Variability of rainfall over the study period (9 % to 96 % probability of non-exceedance) does not translate into equivalent variability in groundwater levels and river baseflow. Groundwater levels, monitored by local communities, persist into the dry season to at least the end of December in most shallow wells, indicating that groundwater is available for irrigation use after the cessation of the wet season. Arguments historically put forward against the promotion of groundwater use for agriculture in SSA on the basis that aquifers are unproductive and irrigation will have unacceptable impacts on wetlands and other groundwater-dependent ecosystems appear exaggerated. It would be unwise to generalise from this case study to the whole of SSA, but useful insights into the wider issues are revealed by the case study approach. We believe there is a case for arguing that shallow groundwater in sub-Saharan Africa represents a neglected opportunity for sustainable intensification of small-scale agriculture.

scholarly journals Modeling soil erosion by data-driven methods using limited input variables

2017 ◽  
Vol 49 (5) ◽  
pp. 1349-1362 ◽  
Author(s):  
Shahla Yavari ◽  
Saman Maroufpoor ◽  
Jalal Shiri
Keyword(s):  
Soil Erosion ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Neuro Fuzzy ◽  
Upland Erosion ◽  
Ann Models ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Development And Management

Abstract Soil is one of the main elements of natural resources. Accurate estimation of soil erosion is very important in optimum soil resources development and management. Analyzing soil erosion by water on cultivated lands is an important task due to the numerous problems caused by erosion. In this study, the performance of three different data-driven approaches, e.g. multilayer perceptron artificial neural network (ANN), grid partitioning (GP), and subtractive neuro-fuzzy (NF) models were evaluated for estimating soil erosion. Land use, slope, soil and upland erosion amount were used as input parameters of the applied models and the erosion values obtained by MPSIAC method were considered as the benchmark for evaluating the ANN and NF models. The applied models were assessed using the coefficient of determination (R2), the root mean square error (RMSE), the BIAS, and the variance accounted for (VAF) indices. The results showed that the subtractive NF model presented the most accurate results with the minimum RMSE value (3.775) and GP, NF and ANN models were ranked successively.

Prediction of Karst Groundwater Level Based on R-Language - Taking Jinci Spring Basin as an Example

2015 ◽  
Vol 730 ◽  
pp. 230-234 ◽  
Author(s):  
Hui Jun Shi ◽  
Xin Qi ◽  
Hua Jin
Keyword(s):  
Groundwater Level ◽  
Multiple Linear Regression Model ◽  
Karst Groundwater ◽  
Groundwater Levels ◽  
R Language ◽  
Study Results ◽  
Groundwater Systems ◽  
Annual Time ◽  
Groundwater Level Prediction

Considering the complexity and randomness of the karst groundwater systems, a multiple linear regression model was developed for groundwater-level prediction based on the R language. The Jinci Spring basin was taken as a case study. Results show that the established model can predict the dynamics of the karst groundwater levels with high accuracy at an annual time scale, which can be served for macroscopic groundwater management.

scholarly journals Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

2021 ◽  
Author(s):  
Andreas Wunsch ◽  
Tanja Liesch ◽  
Stefan Broda
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Neural Networks ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Anthropogenic Factors ◽  
Climate Projections ◽  
Climate Signal ◽  
Groundwater Levels ◽  
Groundwater Level Prediction

Abstract In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st century. We apply a machine learning groundwater level prediction framework, based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under the RCP8.5 scenario, based on six selected climate projections, which represent 80% of the bandwidth of the possible future climate signal for Germany. We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. We detected significant declining trends of groundwater levels for most of the sites, revealing a spatial pattern of stronger decreases especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.

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