Prediction of Water Consumption for New York city using Machine Learning

Aggarwal, Sachin; Sehgal, Smriti

doi:10.1109/spin52536.2021.9565939

Cited by 5 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SVM was established by Corinna Cortes and Vladimir Vapnik in 1995 [49]. SVMs have demonstrated efficacy across several areas because they can identify ideal hyperplanes for class separation in feature space, even in datasets with high dimensions.…”

Section: Support Vector Machinementioning

confidence: 99%

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Niyongabo,

Zhang,

Guan

et al. 2024

Water

View full text Add to dashboard Cite

Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R2 and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand. It is worth noting that accurate predictions of water quantity and quality are essential for sustainable resource management, public health protection, and ecological preservation. Such promising research could significantly enhance urban water demand planning and water resource management.

show abstract

Section: Support Vector Machinementioning

confidence: 99%

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Niyongabo,

Zhang,

Guan

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

“…Aggarwal and Sehgal [24] conducted a comparative study using machine learning algorithms with attribute selection techniques, fold cross-validation, and preprocessing techniques. The authors used lasso regression, ridge regression, XGBoost, least-square SVM, and the hybrid model (Lasso regression+XGBoost).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Table 1 shows a comparative summary of the discussed research works. Linear regression, ridge regression, lasso regression,kernel ridge regression,baysian ridge regression, BPNN,DT, SVM, RF, Ada Boost, GBDT GBDT (IPS) MSE = 0.00000016 MAE = 0.00032787 R 2 = 99.999% (EPS) MSE = 0.00006178 MAE = 0.00584230 R 2 = 99.9578% BEIJING-Tianjin-Hebei region annual water report [24] XGBoost, LSSVR, lasso regression, ridge regression, Proposed (Lasso regression + XGBoost)…”

Section: Literature Reviewmentioning

confidence: 99%

An Artificial Neural Network Model for Water Quality and Water Consumption Prediction

Rustam

Ishaq

Kokab

et al. 2022

Water

View full text Add to dashboard Cite

With rapid urbanization, high rates of industrialization, and inappropriate waste disposal, water quality has been substantially degraded during the past decade. So, water quality prediction, an essential element for a healthy society, has become a task of great significance to protecting the water environment. Existing approaches focus predominantly on either water quality or water consumption prediction, utilizing complex algorithms that reduce the accuracy of imbalanced datasets and increase computational complexity. This study proposes a simple architecture of neural networks which is more efficient and accurate and can work for predicting both water quality and water consumption. An artificial neural network (ANN) consisting of one hidden layer and a couple of dropout and activation layers is utilized in this regard. The approach is tested using two datasets for predicting water quality and water consumption. Results show a 0.96 accuracy for water quality prediction which is better than existing studies. A 0.99 R2 score is obtained for water consumption prediction which is superior to existing state-of-the-art approaches.

show abstract

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

Tesfay Abraha,

Assefa Woldeamanuel,

Gebremariam Beyene

2024

npj Clean Water

View full text Add to dashboard Cite

To develop a water conscious strategy, it is critical to track and trace water from its source to the end users, understand water conservation behaviors, and identify the factors that influence water consumption. However, in developing nations, little research has been done to provide a quantitative picture of how water is consumed and transformed in urban households, as well as the water sensitive interventions needed to improve access to clean water. Hence, the main objective of the study was to determine the most significant residential water consumption variables and to predict residential water consumption in a way that can generate water consumption information for water sensitive intervention decision making using the case study of Adama city in Ethiopia. A combination of top down and bottom up data collection techniques were employed as the data collection instrument. Machine learning was integrated with spatial and socioeconomic analytic techniques to estimate daily household water consumption and identify the factors that significantly influence household water consumption. The results show that there is only “one source option” for the city’s clean water supply and that different water harvesting methods are not likely to be developed. The average daily water consumption per person is 69 liters which falls below the national standard of 80 liters allocated per person per day. The result reveals that the water distribution network covers only 45% of the city master plan. About 38% of the water demand is unmet and 30% of households only receive water once every three days or fewer. This shows that the city is experiencing physical and economic water scarcity. The results demonstrated that family size, housing quality, income, number of rooms, legal status of the parcel, supply reliability, climate, and topographical features are the most important factors in predicting residential water consumption. This study further demonstrates how well supervised machine learning models, such as the Random Forest Regression algorithm, can predict the household’s daily water consumption. The findings also showed that there is a need for significant improvements in water saving habits of the households. Another conclusion that can be drawn is that as long as the city’s business as usual water consumption practice doesn’t change, the water supply problem will worsen over time.

show abstract

Prediction of Water Consumption for New York city using Machine Learning

Cited by 5 publications

References 15 publications

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

An Artificial Neural Network Model for Water Quality and Water Consumption Prediction

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

Contact Info

Product

Resources

About