The Effects of Climate Change on the Reuse of Agricultural Drainage Water in Irrigation

Ashu, Agbortoko Bate; S, Lee

doi:10.1007/s12205-021-0004-2

Cited by 6 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on this coefficient, the actual ecological water demand of soybean and corn in the target irrigated area was 1.235 and 2.372 m 3 , respectively. e total water demand reached 3.607 m 3 . By contrast, our precision irrigation intelligent control system can elevate the utilization coefficient to 0.85.…”

Section: Experiments and Results Analysismentioning

confidence: 98%

“…On this basis, the actual ecological water demand of soybean and corn in the target irrigated area was 3.464 and 3.375 m 3 , respectively. e total water demand reached 6.839 m 3 .…”

Section: Experiments and Results Analysismentioning

confidence: 98%

“…Modern agriculture aims to achieve a high yield of high-quality crops in an ideal cycle. To promote agricultural development in China, it is very meaningful to reconstruct the irrigation water facilities in large irrigated areas, trying to detect an agricultural situation in real time, make reasonable plans for irrigation water consumption, and realize automatic water supply [3][4][5]. In irrigated areas, the intelligent management and scientific decision-making of agricultural irrigation is premised on the accurate estimation of the ecological water demand for different crops under different spatiotemporal conditions [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

[Retracted] Intelligent Control of Agricultural Irrigation through Water Demand Prediction Based on Artificial Neural Network

Cheng

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In irrigated areas, the intelligent management and scientific decision-making of agricultural irrigation are premised on the accurate estimation of the ecological water demand for different crops under different spatiotemporal conditions. However, the existing estimation methods are blind, slow, or inaccurate, compared with the index values of the water demand collected in real time from irrigated areas. To solve the problem, this paper innovatively introduces the spatiotemporal features of ecological water demand to the forecast of future water demand by integrating an artificial neural network (ANN) for water demand prediction with the prediction indices of water demand. Firstly, the ecological water demand for agricultural irrigation of crops was calculated, and a radial basis function neural network (RBFNN) was constructed for predicting the water demand of agricultural irrigation. On this basis, an intelligent control strategy was presented for agricultural irrigation based on water demand prediction. The structure of the intelligent control system was fully clarified, and the main program was designed in detail. The proposed model was proved effective through experiments.

show abstract

Section: Experiments and Results Analysismentioning

confidence: 98%

“…On this basis, the actual ecological water demand of soybean and corn in the target irrigated area was 3.464 and 3.375 m 3 , respectively. e total water demand reached 6.839 m 3 .…”

Section: Experiments and Results Analysismentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

[Retracted] Intelligent Control of Agricultural Irrigation through Water Demand Prediction Based on Artificial Neural Network

Cheng

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The region has a temperate climate with four distinct seasons and a temperature and precipitation of 12 • C and 1321 mm per annum, respectively. The majority of the annual precipitation occurs in the monsoon season (June to September), contributing to approximately 70% of the water availability [7,46,47]. The study area consists of a forest area (32.3%), an urban area (26.1%), paddy rice area (18.3%), barren land (8.1%), upland rice area (6.2%), and grassland and water area (3.5%) [4,46].…”

Section: Area Of Studymentioning

confidence: 99%

“…Agriculture consumed 15.9 billion (m 3 ) of water in 2007, accounting for around 48% of the Republic of Korea's total annual water usage [4,5]. Moreover, it was reported that by 2020, water scarcity would reach more than 4.4 billion m 3 [6,7]. According to a report of the Comprehensive Water Resources Plan (Water Vision 2020), the agricultural water demand in South Korea is significant, with paddy rice water demand accounting for 34.5% (12.90 Gm 3 /y) of the total freshwater resources (37.35 Gm 3 /y) in 2011 [8,9].…”

Section: Introductionmentioning

confidence: 99%

Simulation-Optimization Model for Conjunctive Management of Surface Water and Groundwater for Agricultural Use

Ashu¹,

S²

2021

Water

Self Cite

View full text Add to dashboard Cite

The conjunctive management of surface water and groundwater resources is essential to sustainably manage water resources. The target study is the Osan watershed, in which approximately 60–70% of rainfall occurs during the summer monsoon in Central South Korea. Surface water resources are overexploited six times as much as groundwater resources in this region, leading to increasing pressure to satisfy the region’s growing agricultural water demand. Therefore, a simulation-optimization (S-O) model at the sub-basin scale is required to optimize water resource allocation in the Osan watershed. An S-O model based on an artificial neural network (ANN) model coupled with Jaya algorithm optimization (JA) was used to determine the yearly conjunctive supply of agricultural water. The objective was to minimize the water deficit in the watershed subject to constraints on the cumulative drawdown in each subarea. The ANN model could predict the behaviour of the groundwater level and facilitate decision making. The S-O model could minimize the water deficit by approximately 80% in response to the gross water demand, thereby proving to be suitable for a conjunctive management model for water resource management and planning.

show abstract

Urban Water Consumption Prediction Based on CPMBNIP

Li,

Song

2023

Water Resour Manage

View full text Add to dashboard Cite

The Effects of Climate Change on the Reuse of Agricultural Drainage Water in Irrigation

Cited by 6 publications

References 50 publications

[Retracted] Intelligent Control of Agricultural Irrigation through Water Demand Prediction Based on Artificial Neural Network

[Retracted] Intelligent Control of Agricultural Irrigation through Water Demand Prediction Based on Artificial Neural Network

Simulation-Optimization Model for Conjunctive Management of Surface Water and Groundwater for Agricultural Use

Urban Water Consumption Prediction Based on CPMBNIP

Contact Info

Product

Resources

About