scholarly journals Corrigendum: Water Supply 20 (3), 909–921: Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer, https://iwaponline.com/ws/article/20/3/909/72227/Comparison-study-of-artificial-intelligence-method?searchresult = 1

2020 ◽  
Vol 20 (6) ◽  
pp. 2440-2440
Author(s):  
Akbar Khedri ◽  
Nasrollah Kalantari ◽  
Meysam Vadiati
Keyword(s):  
Artificial Intelligence ◽  
Water Supply ◽  
Groundwater Level ◽  
Unconfined Aquifer ◽  
Comparison Study ◽  
Short Term ◽  
Groundwater Level Prediction

Groundwater Level Prediction Using Support Vektor Machines and Autoregressive (AR) Modelss

2017 ◽  
Author(s):  
Fatih Üneş ◽  
Mustafa Demirci ◽  
Yunus Ziya Kaya ◽  
Eyup Ispir ◽  
Mustafa Mamak
Keyword(s):  
Water Resources ◽  
Water Supply ◽  
Ground Water ◽  
Water Level ◽  
Groundwater Level ◽  
Irrigation Water ◽  
Ground Water Level ◽  
Water Level Fluctuations ◽  
Amik Plain ◽  
Groundwater Level Prediction

Water resources managers can benefit from accurate prediction of the availability of groundwater. Ground water is a major source of water in Turkey for irrigation, water supply and industrial uses. The ground water level fluctuations depend on several factors such as rainfall, temperature, pumping etc. In this study, Hatay Amik Plain, Kumlu region was evaluated using Autoregressive (AR) and Support Vektor Machines (SVMs) methods. The monthly groundwater level was used the previous years data belonging to the Kumlu region.

scholarly journals A Novel Deep Learning Algorithm for Groundwater Level Prediction based on Spatiotemporal Attention Mechanism

2020 ◽  
Author(s):  
Chong Chen ◽  
Han Zhou ◽  
Hui Zhang ◽  
Lulu Chen ◽  
Zhu Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Groundwater Level ◽  
Learning Algorithm ◽  
Attention Mechanism ◽  
Short Term ◽  
Groundwater Levels ◽  
Deep Learning Algorithm ◽  
Term Prediction ◽  
Groundwater Level Prediction

Abstract Groundwater resources play a vital role in production, human life and economic development. Effective prediction of groundwater levels would support better water resources management. Although machine learning algorithms have been studied and applied in many domains with good enough results, the researches in hydrologic domains are not adequate. This paper proposes a novel deep learning algorithm for groundwater level prediction based on spatiotemporal attention mechanism. Short-term (one month ahead) and long-term (twelve months ahead) prediction of groundwater level are conducted with observed groundwater levels collected from several boreholes in the middle reaches of the Heihe River Basin in northwestern China. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to evaluate the performance of the proposed algorithm and several baseline models (i.e., SVR, Support Vector Regression; FNN, Feedforward Neural Networks; LSTM, Long Short-Term Memory neural network). The results show that the proposed model can effectively improve the prediction accuracy compared to the baseline models with MAE of 0.0754, RMSE of 0.0952 for short-term prediction and MAE of 0.0983, RMSE of 0.1215 for long-term prediction. This study provides a feasible and accurate approach for groundwater prediction which may facilitate decision making for water management.

Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer

2020 ◽  
Vol 20 (3) ◽  
pp. 909-921 ◽  
Author(s):  
Akbar Khedri ◽  
Nasrollah Kalantari ◽  
Meysam Vadiati
Keyword(s):  
Groundwater Level ◽  
Unconfined Aquifer ◽  
Least Square ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Data Set ◽  
Groundwater Levels ◽  
Statistical Performance Evaluation ◽  
Groundwater Level Prediction

Abstract Accurate and reliable groundwater level prediction is an important issue in groundwater resource management. The objective of this research is to compare groundwater level prediction of several data-driven models for different prediction periods. Five different data-driven methods are compared to evaluate their performances to predict groundwater levels with 1-, 2- and 3-month lead times. The four quantitative standard statistical performance evaluation measures showed that while all models could provide acceptable predictions of groundwater level, the least square support vector machine (LSSVM) model was the most accurate. We developed a set of input combinations based on different levels of groundwater, total precipitation, average temperature and total evapotranspiration at monthly intervals. For each model, the antecedent inputs that included Ht-1, Ht-2, Ht-3, Tt, ETt, Pt, Pt-1 produced the best-fit model for 1-month lead time. The coefficient of determination (R2) and the root mean square error (RMSE) were calculated as 0.99%, 1.05 meters for the train data set, and 95%, 2.3 meters for the test data set, respectively. It was also demonstrated that many combinations the above-mentioned approaches could model groundwater levels for 1 and 2 months ahead appropriately, but for 3 months ahead the performance of the models was not satisfactory.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

2021 ◽  
Vol 15 (1) ◽  
pp. 1147-1158
Author(s):  
Shahab S. Band ◽  
Essam Heggy ◽  
Sayed M. Bateni ◽  
Hojat Karami ◽  
Mobina Rabiee ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Gaussian Process ◽  
Groundwater Level ◽  
Gaussian Process Regression ◽  
Arid Areas ◽  
Groundwater Level Prediction

scholarly journals Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Polymers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Naruki Hagiwara ◽  
Shoma Sekizaki ◽  
Yuji Kuwahara ◽  
Tetsuya Asai ◽  
Megumi Akai-Kasaya
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
High Speed ◽  
Conductive Polymer ◽  
Long Term Potentiation ◽  
Short Term ◽  
Synaptic Functions ◽  
Term Potentiation ◽  
Artificial Brain ◽  
External Stimuli

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

Machine-learning-based regional-scale groundwater level prediction using GRACE

2021 ◽  
Vol 29 (3) ◽  
pp. 1027-1042 ◽  
Author(s):  
Pragnaditya Malakar ◽  
Abhijit Mukherjee ◽  
Soumendra N. Bhanja ◽  
Ranjan Kumar Ray ◽  
Sudeshna Sarkar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Groundwater Level ◽  
Regional Scale ◽  
Groundwater Level Prediction

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

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