Irrigation Water Demand Forecasting Using Wavelet Transforms and Artificial Intelligence

2011 ◽  
Author(s):  
Jan Franklin Adamowski ◽  
Hiu Fung Chan ◽  
Inmaculada Pulido-Calvo
Keyword(s):  
Artificial Intelligence ◽  
Irrigation Water ◽  
Water Demand ◽  
Wavelet Transforms ◽  
Demand Forecasting ◽  
Water Demand Forecasting ◽  
Irrigation Water Demand

Optimisation of water demand forecasting by artificial intelligence with short data sets

2019 ◽  
Vol 177 ◽  
pp. 59-66 ◽  
Author(s):  
Rafael González Perea ◽  
Emilio Camacho Poyato ◽  
Pilar Montesinos ◽  
Juan Antonio Rodríguez Díaz
Keyword(s):  
Artificial Intelligence ◽  
Water Demand ◽  
Demand Forecasting ◽  
Data Sets ◽  
Water Demand Forecasting

Predicting water demand: a review of the methods employed and future possibilities

2019 ◽  
Vol 19 (8) ◽  
pp. 2179-2198 ◽  
Author(s):  
Gustavo de Souza Groppo ◽  
Marcelo Azevedo Costa ◽  
Marcelo Libânio
Keyword(s):  
Artificial Intelligence ◽  
Water Supply ◽  
Water Demand ◽  
Supply And Demand ◽  
Demand Forecasting ◽  
System Management ◽  
Water Demand Forecasting ◽  
Combination Of Methods ◽  
Applications Of Machine Learning

Abstract The balance between water supply and demand requires efficient water supply system management techniques. This balance is achieved through operational actions, many of which require the application of forecasting concepts and tools. In this article, recent research on urban water demand forecasting employing artificial intelligence is reviewed, aiming to present the ‘state of the art’ on the subject and provide some guidance regarding methods and models to research and professional sanitation companies. The review covers the models developed using standard statistical techniques, such as linear regression or time-series analysis, or techniques based on Soft Computing. This review shows that the studies are, mostly, focused on the management of the operating systems. There is, therefore, room for long-term forecasts. It is worth noting that there is no global model that surpasses all the methods for all cases, it being necessary to study each region separately, evaluating the strengths of each model or the combination of methods. The use of statistical applications of Machine Learning and Artificial Intelligence methodologies has grown considerably in recent years. However, there is still room for improvement with regard to water demand forecasting.

scholarly journals Forecast of irrigation water demand considering multiple factors

2015 ◽  
Vol 368 ◽  
pp. 331-336
Author(s):  
X. Wang ◽  
X. Lei ◽  
X. Guo ◽  
J. You ◽  
H. Wang
Keyword(s):  
Irrigation Water ◽  
Water Demand ◽  
Demand Forecasting ◽  
Impact Factors ◽  
Haihe River Basin ◽  
Trend Prediction ◽  
Multiple Factors ◽  
Irrigation Water Demand ◽  
Study Results ◽  
Basin Scale

Abstract. Many factors influence irrigation water requirement on the basin scale, which make it difficult to obtain comprehensive data. Despite the advantage of less needing historical data, the prediction precision of traditional trend prediction methods is hard to guarantee. For water scarce basins, the artificial influence on irrigation requirement should be thought of as important impact factors. In this paper, the PCA (principal component analysis) method is used to identify the main influencing factors, such as precipitation, irrigation area, water saving technology and so on. Based on that, an irrigation water demand prediction model considering multiple factors is developed for water shortage regions. The method is applied in the Haihe River basin as an example. The study results show that the irrigation water demand forecasting method considering multiple factors in this paper can achieve higher modelling accuracy, compared with the traditional trend prediction method and the method that does not consider the human influence. In view of the small average relative error, 1.32%, it has good values for application.

scholarly journals Econometric Forecasting of Irrigation Water Demand Conserves a Valuable Natural Resource

2013 ◽  
Vol 45 (3) ◽  
pp. 557-568 ◽  
Author(s):  
Swagata “Ban” Banerjee ◽  
Babatunde A. Obembe
Keyword(s):  
Natural Resource ◽  
Irrigation Water ◽  
Water Demand ◽  
Crop Production ◽  
Demand Forecasting ◽  
Pilot Project ◽  
Relative Strength ◽  
Physical Models ◽  
Institutional Conditions ◽  
Irrigation Water Demand

Natural causes (such as droughts), non-natural causes (such as competing uses), and government policies limit the supply of water for agriculture in general and irrigating crops in particular. Under such reduced water supply scenarios, existing physical models reduce irrigation proportionally among crops in the farmer's portfolio, disregarding temporal changes in economic and/or institutional conditions. Hence, changes in crop mix resulting from expectations about risks and returns are ignored. A method is developed that considers those changes and accounts for economic substitution and expansion effects. Forecasting studies based on this method with surface water in Georgia and Alabama demonstrate the relative strength of econometric modeling vis-à-vis physical methods. Results from a study using this method for ground water in Mississippi verify the robustness of those findings. Results from policy-induced simulation scenarios indicate water savings of 12% to 27% using the innovative method developed. Although better irrigation water demand forecasting in crop production was the key objective of this pilot project, conservation of a valuable natural resource (water) has turned out to be a key consequence.

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