Optimal Cultivation Pattern to Increase Revenue and Reduce Water Use: Application of Linear Programming to Arjan Plain in Fars Province

Daghighi, Amin; Nahvi, Ali; Kim, Ungtae

doi:10.3390/agriculture7090073

Cited by 41 publications

(17 citation statements)

References 19 publications

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“…The optimization methods based on intelligent methods are dependent on the group of direct methods and are very similar to stochastic search methods. Stochastic search methods only enjoy general search strategy, while methods based on artificial intelligence run general and local search strategy simultaneously (Daghighi et al, 2017). Indeed, intelligent methods are intelligent dynamic systems based on experimental data, which transfer the knowledge or law beyond data to the network structure by processing experimental data.…”

Section: Overview Of Intelligent Methodsmentioning

confidence: 99%

Forecasting the Environmental Parameters of Water Resources Using Machine Learning Methods

Faraj¹,

Shen²

2018

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Careful monitoring of the quality and quantity of water resources plays a significant role in modern environmental management. Thus, to achieve this aim, the quantitative and qualitative parameters of water resources should be measured and controlled with a desirable accuracy. However, it is not always possible to measure these parameters with easy, inexpensive, precise, and quick experimental methods. Therefore, today to solve this type of problems, new methods including smart methods are used which have a great potential in many computational areas. Considering the variety of the studies and absence of a comprehensive review paper, this research should be conducted. The aim of this paper is to comprehensively review application of the smart methods of artificial neural network and support vector machine in the area of water resources and to develop a comprehensive study source for the researchers interested in this field. The results of these studies all show that these advanced smart methods are more efficient, accurate, economical, and faster than other computational methods to predict quantitative and qualitative parameters of water resources.

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Section: Overview Of Intelligent Methodsmentioning

confidence: 99%

Forecasting the Environmental Parameters of Water Resources Using Machine Learning Methods

Faraj¹,

Shen²

2018

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“…Interferometric Synthetic Aperture Radar (InSAR) technique is a geodetic tool to image ground displacement in centimeter-scale and can be a very helpful technique in understanding the earthquakes, volcanos and glaciers [41]. InSAR can also benefit geomorphologists and hydrologist by providing an accurate measurement of slope motion, sediment erosion and deposition, water level fluctuation and soil moisture content [42][43][44][45][46]. InSAR has been considered as a powerful method to monitor ground surface deformations [47] and is an alternative technique to measure surface displacement.…”

Section: Introductionmentioning

confidence: 99%

How Groundwater Level Fluctuations and Geotechnical Properties Lead to Asymmetric Subsidence: A PSInSAR Analysis of Land Deformation over a Transit Corridor in the Los Angeles Metropolitan Area

et al. 2019

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Los Angeles has experienced ground deformations during the past decades. These ground displacements can be destructive for infrastructure and can reduce the land capacity for groundwater storage. Therefore, this paper seeks to evaluate the existing ground displacement patterns along a new metro tunnel in Los Angeles, known as the Sepulveda Transit Corridor. The goal is to find the most crucial areas suffering from subsidence or uplift and to enhance the previous reports in this metropolitan area. For this purpose, we applied a Persistent Scatterer Interferometric Synthetic Aperture Radar using 29 Sentinel-1A acquisitions from June 2017 to May 2018 to estimate the deformation rate. The assessment procedure demonstrated a high rate of subsidence in the Inglewood field that is near the study area of the Sepulveda Transit Corridor with a maximum deformation rate of 30 mm/yr. Finally, data derived from in situ instruments as groundwater level variations, GPS observations, and soil properties were collected and analyzed to interpret the results. Investigation of geotechnical boreholes indicates layers of fine-grained soils in some parts of the area and this observation confirms the necessity of more detailed geotechnical investigations for future constructions in the region. Results of investigating line-of-sight displacement rates showed asymmetric subsidence along the corridor and hence we proposed a new framework to evaluate the asymmetric subsidence index that can help the designers and decision makers of the project to consider solutions to control the current subsidence.

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“…A real time rainfall estimation helps to predict groundwater recharge estimations under different hydrological conditions and plays even a vital role in agriculture water management (e.g. [2] [3]). Rain gauges provide rainfall measurements at individual points but different uncertainties are associated with the use of gauge data to estimate rainfall with appropriate temporal and spatial scale variation at basin scale [4].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model

Andiego¹,

Waseem²,

Usman³

et al. 2018

CWEEE

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Rain gauge data suffers from spatial errors because of precipitation variability within short distances and due to sparse or irregular network. Use of interpolation is often unreliable to evaluate due to the aforementioned irregular sparse networks. This study is carried out in the Nette River catchment of Lower Saxony to alleviate the problem of using gauge data to measure the performance of interpolation. Radar precipitation data was extracted in the positions of 53 rain gauge stations, which are distributed throughout the range of the weather surveillance radar (WSR). Since radar data traditionally suffers from temporal errors, it was corrected using the Mean Field Bias (MFB) method by utilizing the rain gauge data and then further used as the reference precipitation in the study. The performances of Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) interpolation methods by means of cross validation were assessed. Evaluation of the effect of the gauge densities on HBV-IWW hydrological model was achieved by comparing the simulated discharges for the two interpolation methods and corresponding densities against the simulated discharge of the reference precipitation data. Interpolation performance in winter was much better than summer for both interpolation methods. Furthermore, Ordinary Kriging performed marginally better than Inverse Distance Weighting in both seasons. In case of areal precipitation, progressive improvement in performance with increase in gauge density for both interpolation methods was observed, but Inverse Distance Weighting was found more consistent up to higher densities. Comparison showed that Ordinary Kriging outperformed Inverse Distance Weighting only up to 70% density, beyond which the performance is equal. The hydrological modelling results are similar to that of areal precipitation except that for both methods, there was no improvement in performance beyond the 50% gauge density.

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Optimal Cultivation Pattern to Increase Revenue and Reduce Water Use: Application of Linear Programming to Arjan Plain in Fars Province

Cited by 41 publications

References 19 publications

Forecasting the Environmental Parameters of Water Resources Using Machine Learning Methods

Forecasting the Environmental Parameters of Water Resources Using Machine Learning Methods

How Groundwater Level Fluctuations and Geotechnical Properties Lead to Asymmetric Subsidence: A PSInSAR Analysis of Land Deformation over a Transit Corridor in the Los Angeles Metropolitan Area

The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model

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