Research graph
References from Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China. Local targets link to admitted publications; unresolved targets remain external evidence.
Domestic water consumption under intermittent and continuous modes of water supply
10.1007/s11269-014-0520-7 · 2014 · External reference
Urban residential water demand prediction based on artificial neural networks and time series models.
10.1007/s11269-015-1021-z · 2015 · External reference
Unresolved reference
2011 · External reference
Comparative analysis of fuzzy inference systems for water consumption time series prediction
10.1016/j.jhydrol.2009.06.013 · 2009 · External reference
Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models
10.1002/wrcr.20517 · 2013 · External reference
An ensemble-learning-based method for short-term water demand forecasting
10.1007/s11269-021-02808-4 · 2021 · External reference
Large metropolitan water demand forecasting using DAN2, FTDNN, and KNN models: A case study of the city of Tehran
10.1080/1573062x.2016.1223858 · 2017 · External reference
Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.
10.3390/ijerph14111386 · 2017 · External reference
Uncertain time series forecasting method for the water demand prediction in Beijing.
10.2166/ws.2021.401 · 2022 · External reference
Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok.
10.1007/s11269-010-9766-x · 2011 · External reference
Hourly water demand forecasting using a hybrid model based on mind evolutionary algorithm
10.2166/ws.2021.228 · 2022 · External reference
Hybrid Models for Water Demand Forecasting
10.1061/(asce)wr.1943-5452.0001331 · 2021 · External reference
Optimization of pumping schedule based on water demand forecasting using a combined model of autoregressive integrated moving average and exponential smoothing
10.2166/ws.2014.104 · 2015 · External reference
Medium and long term water demand prediction in liaoning province based on grey system prediction model and the quota method prediction model
2013 · External reference
Trend and regression analysis-based water demand prediction of Pearl River Delta urban agglomeration
2017 · External reference
Water demand in watershed forecasting using a hybrid model based on autoregressive moving average and deep neural networks
10.1007/s11356-022-22943-8 · 2023 · External reference
Trend analysis and prediction on water consumption in southwestern ethiopia.
10.1155/2022/3294954 · 2022 · External reference
A novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks
10.1016/j.jhydrol.2018.03.047 · 2018 · External reference
Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization.
10.1016/j.cor.2018.01.013 · 2018 · External reference
Domestic water demand prediction based on system dynamics combined with social-hydrology methods
10.2166/nh.2022.051 · 2022 · External reference
A Forecasting Framework Based on Kalman Filter Integrated Multivariate Local Polynomial Regression: Application to Urban Water Demand
10.1007/s11063-019-10001-3 · 2019 · External reference
Prediction and analysis of water resources demand in Taiyuan City based on principal component analysis and BP neural network.
2021 · External reference
Application of the extreme learning machine algorithm for the prediction of monthly effective drought index in eastern Australia
10.1016/j.atmosres.2014.10.016 · 2015 · External reference
Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data
10.1080/10106049.2022.2158951 · 2023 · External reference
Using extreme learning machines for short-term urban water demand forecasting
10.1080/1573062x.2016.1236133 · 2017 · External reference
Predicting water demand: a review of the methods employed and future possibilities
10.2166/ws.2019.122 · 2019 · External reference
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders
10.1016/j.compbiomed.2013.01.020 · 2013 · External reference
Evolving support vector machines using fruit fly optimization for medical data classification.
10.1016/j.knosys.2016.01.002 · 2016 · External reference
Medium-term urban water demand forecasting with limited data using an ensemble wavelet-bootstrap machine-learning approach
10.1061/(asce)wr.1943-5452.0000454 · 2015 · External reference
Machine learning for modeling water demand
10.1061/(asce)wr.1943-5452.0001067 · 2019 · External reference
Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method.
10.3390/su13073665 · 2021 · External reference
Adaptive ranking based ensemble learning of Gaussian process regression models for quality-related variable prediction in process industries
10.1016/j.asoc.2020.107060 · 2021 · External reference
Water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system.
10.1016/j.envres.2022.112942 · 2022 · External reference
Logarithmic Mean Divisia Index (LMDI) decomposition analysis of changes in agricultural water use: a case study of the middle reaches of the Heihe River basin, China.
10.1016/j.agwat.2018.06.041 · 2018 · External reference
Evaluating and mapping water supply and demand for sustainable urban ecosystem management in Shenzhen, China
10.1016/j.jclepro.2019.119754 · 2020 · External reference
Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province
10.1007/s11356-022-24604-2 · 2023 · External reference
Optimization of urban industrial structure under the low-carbon goal and the water constraints: a case in Dalian, China.
10.1016/j.jclepro.2015.09.056 · 2016 · External reference
Urban water consumption and its influencing factors in China: Evidence from 286 cities
10.1016/j.jclepro.2017.08.044 · 2017 · External reference
Analyses of the changing process and influencing factors of water resource utilization in Megalopolis of Arid Area
10.1134/s0097807815050176 · 2015 · External reference
A new technique to estimate regional irrigation water demand and driving factor effects using an improved SWAT model with LMDI factor decomposition in an arid basin
10.1016/j.jclepro.2018.03.056 · 2018 · External reference
Identifying Residential Water End Uses Underpinning Peak Day and Peak Hour Demand
10.1061/(asce)wr.1943-5452.0000357 · 2014 · External reference
Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models predicting advance rate of TBM
10.1016/j.trgeo.2022.100819 · 2022 · External reference
Unresolved reference
1997 · External reference
A Random Forest-Based Method for Predicting Borehole Trajectories.
10.3390/math11061297 · 2023 · External reference
Comparison of arima and artificial neural networks models for stock price prediction.
10.1155/2014/614342 · 2014 · External reference
Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals
10.1016/j.heliyon.2019.e02796 · 2019 · External reference
Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China.
10.3390/e23070882 · 2021 · External reference
Global threats to human water security and river biodiversity
10.1038/nature09440 · 2010 · External reference
Water security: research challenges and opportunities
10.1126/science.1226337 · 2012 · External reference
Econometric analysis of industrial water use efficiency in China.
10.1007/s10668-014-9601-2 · 2015 · External reference
Predicting impact of urbanization on water resources in megacity Delhi
10.1016/j.rsase.2020.100361 · 2020 · External reference
Potential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas
10.1080/10106049.2016.1206974 · 2017 · External reference
