Machine Learning for Modeling Water Demand

Villarín, María C.; Rodríguez-Galiano, Víctor

doi:10.1061/(asce)wr.1943-5452.0001067

Cited by 44 publications

(18 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analyzing water demand time-series data has taken many forms, such as regression, autoregression, autoregressive integrated moving average (ARIMA), exponential smoothing, and machine learning methods [e.g., support vector machines (SVMs), artificial neural networks (ANNs), classification and regression trees, and random forests (Villarin and Rodriguez-Galiano 2019)]. SVM and ANN are among the most popular methods (Msiza et al 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Urban Water Demand: Statistical Optimization Approach to Modeling Daily Demand

Capt

Mirchi

Kumar³

et al. 2021

J. Water Resour. Plann. Manage.

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Urban Water Demand: Statistical Optimization Approach to Modeling Daily Demand

Capt

Mirchi

Kumar³

et al. 2021

J. Water Resour. Plann. Manage.

View full text Add to dashboard Cite

“…Furthermore, statistical models often fail to effectively deal with complex data relationships; their prediction accuracy also decreases with an increase in the amount of data [31]. Other methods should be employed when dealing with big and complex data [32]. For example, Rozos et al [33] employed an integrated system dynamics and cellular automata model to predict water demand under alternative approaches, including distributed water infrastructure.…”

Section: Models For Predicting Water Demandmentioning

confidence: 99%

“…Parisouj et al [47] employed support vector regression, artificial neural network with backpropagation, and extreme learning machine to predict the monthly and daily streamflows of four river basins in the United States. Villarin and Rodriguez-Galiano [32] used classification and regression trees and random forest to establish a multivariate prediction model for water demand in Seville, Spain. Sengupta et al [48] used support vector machine, artificial neural network, and random forest to predict changes in stream channel morphology.…”

Section: Models For Predicting Water Demandmentioning

confidence: 99%

Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China

Shuang

Zhao

2021

Water

View full text Add to dashboard Cite

Predicting water demand helps decision-makers allocate regional water resources efficiently, thereby preventing water waste and shortage. The aim of this study is to predict water demand in the Beijing–Tianjin–Hebei region of North China. The explanatory variables associated with economy, community, water use, and resource availability were identified. Eleven statistical and machine learning models were built, which used data covering the 2004–2019 period. Interpolation and extrapolation scenarios were conducted to find the most suitable predictive model. The results suggest that the gradient boosting decision tree (GBDT) model demonstrates the best prediction performance in the two scenarios. The model was further tested for three other regions in China, and its robustness was validated. The water demand in 2020–2021 was provided. The results show that the identified explanatory variables were effective in water demand prediction. The machine learning models outperformed the statistical models, with the ensemble models being superior to the single predictor models. The best predictive model can also be applied to other regions to help forecast water demand to ensure sustainable water resource management.

show abstract

“…Thus, it may be challenge to common statistical techniques interpret them. Given their technical character, superior performance, ease of visual interpretation, and implementation availability with R software, machine learning techniques are considered as an alternative approach to conventional statistical models [20].…”

Section: Selection Of Explanatory Variables and Classification Of Drought Perceptionmentioning

confidence: 99%

Use of Machine Learning in Evaluation of Drought Perception in Irrigated Agriculture: The Case of an Irrigated Perimeter in Brazil

Xavier

Silva

Carvalho

et al. 2020

Water

View full text Add to dashboard Cite

This study aimed to understand the perception of drought among farmers, in order to support decision-making in the water allocation process. This study was carried out in the Tabuleiro de Russas irrigated perimeter, in northeast Brazil, over the drought period of 2012–2018. Two analyses were conducted: (i) drought characterization, using the Standardized Precipitation Index (SPI) based on drought duration and frequency criteria; and (ii) analysis of farmers’ perceptions of drought via selection of explanatory variables using the Random Forest (RF) and the Decision Tree (DT) methods. The 2012–2018 drought period was defined as a meteorological phenomenon by local farmers; however, an SPI evaluation indicated that the drought was of a hydrological nature. According to the RF analysis, four of the nine study variables were more statistically important than the others in influencing farmers’ perception of drought: number of cultivated land plots, farmer’s age, years of experience in the agriculture sector, and education level. These results were confirmed using DT analysis. Understanding the relationship between these variables and farmers’ perception of drought could aid in the development of an adaptation strategy to water deficit scenarios. Farmers’ perception can be beneficial in reducing conflicts, adopting proactive management practices, and developing a holistic and efficient early warning drought system.

show abstract

Machine Learning for Modeling Water Demand

Cited by 44 publications

References 73 publications

Urban Water Demand: Statistical Optimization Approach to Modeling Daily Demand

Urban Water Demand: Statistical Optimization Approach to Modeling Daily Demand

Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China

Use of Machine Learning in Evaluation of Drought Perception in Irrigated Agriculture: The Case of an Irrigated Perimeter in Brazil

Contact Info

Product

Resources

About