Analysis of driving factors of water demand based on explainable artificial intelligence

Ou, Zhigang; He, Fan; Zhu, Yongnan; Lu, Peiyi; Wang, Lichuan

doi:10.1016/j.ejrh.2023.101396

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…Liu et al [26] evaluated the spatial variation in the level of urbanization across provinces in China and analyzed the influences of urbanization on regional water consumption. Notably, most of these studies have focused on the national or regional scales [24,27,28], and the correlation between water consumption and socio-economic development on the urban scale remains relatively unexplored.…”

Section: Stagementioning

confidence: 99%

Evolution in Patterns of Urban Water Consumption Accompanying Socio-Economic Development

He,

Ma,

Wang

et al. 2024

Sustainability

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To explore the evolution in patterns of urban water consumption (UWC) accompanying socio-economic development, historical data on socio-economic conditions and water consumption in developed cities worldwide were collected. stages of evolution and patterns in UWC were identified through Pettitt tests. Through correlation analysis, the main socio-economic indicators influencing UWC were identified, and their threshold values for different trends in the evolution of UWC were statistically analyzed. The use of these patterns of evolution for UWC prediction was explored taking Changsha, China as a case study. Results indicate the following: (1) UWC typically increases in the early stage of development and then stabilizes or decreases later; (2) when UWC stabilizes or decreases, socio-economic indicators, namely per capita GDP, tertiary industry’s contribution to GDP, and urbanization rate, range from [USD 10,000, 60,000], [60%, 80%], and [85%, 95%], respectively, entering or approaching the developed economy stage. (3) Using patterns in this evolution, Changsha’s UWC is predicted to increase until the turning point, which will occur between 2027 and 2035 (earlier than that predicted using genetic programming) and then decrease earlier than that of Hunan Province or the entirety of China. Predicted values are lower than those obtained using the quota method. This study provides a new perspective on relationships between UWC and socio-economic conditions, supporting water resources planning.

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Section: Stagementioning

confidence: 99%

Evolution in Patterns of Urban Water Consumption Accompanying Socio-Economic Development

He,

Ma,

Wang

et al. 2024

Sustainability

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Tomato Health Monitoring System: Tomato Classification, Detection, and Counting System Based on YOLOv8 Model With Explainable MobileNet Models Using Grad-CAM++

Quach,

Quoc,

Quynh

et al. 2024

IEEE Access

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Fruits and vegetables (especially, tomatoes) healthy detection are important tasks for smart agriculture. Several works have been published in tomato detection, however, there is little research on using explainable AI to detect, classify and count tomato fruit status. In this work, we propose a Tomatoes Health Check System by evaluating MobileNet models based on the physiological tomato dataset. The MobileNet model is considered a model specifically designed to operate effectively on mobile devices and limited resources. Therefore, MobileNet models used in applying explainable artificial intelligence (XAI) problems must be considered. Our research conducts experiments to evaluate the accuracy of the MobileNets, MobileNetV2 and MobileNetV3 models based on the evaluation metrics; the highest accuracy of 96.69% belongs to the MobileNetV3 model. To evaluate the reliability of the model used, we use Grad-CAM++ to explain and evaluate reliability based on the Intersection over the Union parameter, resulting in MobileNetV2 being the model with the highest value with 100.00%(δ=0), 100.00% (δ=0.25) and 98.89% (δ=0.5). Our research conducted an evaluation experiment using the Simple online and real-time tracking algorithm to combine the YOLOv8 and MobileNetV2 algorithms to detect, classify and count the number of tomatoes according to physiology using video. Finally, we use the research results to build an application system.

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Analysis of driving factors of water demand based on explainable artificial intelligence

Cited by 2 publications

References 55 publications

Evolution in Patterns of Urban Water Consumption Accompanying Socio-Economic Development

Evolution in Patterns of Urban Water Consumption Accompanying Socio-Economic Development

Tomato Health Monitoring System: Tomato Classification, Detection, and Counting System Based on YOLOv8 Model With Explainable MobileNet Models Using Grad-CAM++

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