Investigation of Forecast Accuracy and its Impact on the Efficiency of Data-Driven Forecast-Based Reservoir Operating Rules by Ehsan Mostaghimzadeh, Seyed Mohammad Ashrafi, Arash Adib, Zong Woo Geem

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Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique by Omar Haji Kombo, Santhi Kumaran, Yahya H. Sheikh, Alastair Bovim, Kayalvizhi Jayavel

“…Experimental results have confirmed the high performance of the proposed model in terms of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe (NSE), and coefficient of determination (<i>R</i>²).…”
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Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model by Hafiza Mamona Nazir, Ijaz Hussain, Muhammad Faisal, Elsayed Elsherbini Elashkar, Alaa Mohamd Shoukry

“…We found that our proposed hybrid SSA-EBT-VMD-SVM model outperformed than others based on following performance measures: the Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). …”
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Hydrological Response to Climate and Land Use Changes in the Dry–Warm Valley of the Upper Yangtze River by Congcong Li, Yanpeng Cai, Zhong Li, Qianqian Zhang, Lian Sun, Xinyi Li, Pengxiao Zhou

“…Subsequently, a deep learning neural network model of the long short-term memory (LSTM) and a traditional multi-model ensemble mean (MMEM) method were used for an ensemble of 31 global climate models (GCMs) for climate projection. …”
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How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies? by Anas El-Alem, Karem Chokmani, Aarthi Venkatesan, Lhissou Rachid, Hachem Agili, Jean-Pierre Dedieu

“…The leave-one-out cross validation technique was applied to evaluate the EBS, at a local scale, where results were satisfactory (R² = Nash = 0.94 and RMSE = 5.6 µg chl_a L⁻¹). …”
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Application of a Hybrid CEEMD-LSTM Model Based on the Standardized Precipitation Index for Drought Forecasting: The Case of the Xinjiang Uygur Autonomous Region, China by Yan Ding, Guoqiang Yu, Ran Tian, Yizhong Sun

“…We propose a hybrid model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) to improve drought prediction accuracy. …”
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Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets by Chongxun Mo, Qihua Su, Xingbi Lei, Rongyong Ma, Yi Huang, Chengxin Feng, Guikai Sun

“…In our experiments, the effectiveness of the EC approach was investigated by runoff modeling to incorporate information from the GR4J model and six precipitation datasets in the Pingtang Watershed (PW; Southwest China), and the single precipitation dataset-based approaches and the ensemble average were used as benchmarks. The results show that the EC method performed better than the benchmarks and had a satisfactory performance with Nash–Sutcliffe values of 0.68 during calibration and validation. …”
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Monitoring Waterlogging Damage of Winter Wheat Based on HYDRUS-1D and WOFOST Coupled Model and Assimilated Soil Moisture Data of Remote Sensing by Jian Zhang, Bin Pan, Wenxuan Shi, Yu Zhang

“…The hydrological model HYDRUS-1D is coupled with the crop growth model WOFOST, and the Ensemble Kalman Filter is used to assimilate Sentinel-1 inversion soil moisture data. …”
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Monthly runoff prediction using modified CEEMD-based weighted integrated model by Xinqing Yan, Yuan Chang, Yang Yang, Xuemei Liu

“…Results indicated that this model performs better than other models, with the Nash–Sutcliffe efficiency (NSE) reaching above 0.92, qualification rate (QR) reaching above 75% and all error indicators being minimal. …”
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Differentiability and retrievability of CO2 and H2O clouds on Mars from MRO/MCS measurements: A radiative-transfer study by Hurley, J, Teanby, N, Irwin, P, Calcutt, S, Sefton-Nash, E

“…In this work, the multiple scattering radiative-transfer tool NemesisMCS has been used to create a large dataset of simulations of CO2 and H2O clouds on Mars as would be measured by MRO/MCS, using a range of atmospheric conditions as well as cloud parameters derived from literature suitable for upper atmospheric clouds, and building specifically on the work of Sefton-Nash et al. (2013). This ensemble of simulations has been used to characterise the spectral signature of plausible CO2 and H2O clouds, as well as to assess the suitability of MRO/MCS to observe, to differentiate between, and to derive properties of such clouds. …”

Improved monthly runoff time series prediction using the SOA–SVM model based on ICEEMDAN–WD decomposition by Dong-mei Xu, Xiang Wang, Wen-chuan Wang, Kwok-wing Chau, Hong-fei Zang

“…RMSE, Mean Absolute Percentage Error (MAPE), Nash-Sutcliffe Efficiency Coefficient (NSEC), and R are selected to evaluate the prediction results and the model is compared with SOA-SVM model, EMD-SOA-SVM model and CEEMDAN-SOA-SVM model other models. …”
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Epistasis at the SARS-CoV-2 Receptor-Binding Domain Interface and the Propitiously Boring Implications for Vaccine Escape by Nash D. Rochman, Guilhem Faure, Yuri I. Wolf, Peter L. Freddolino, Feng Zhang, Eugene V. Koonin

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Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan by Muhammad Tayyab, Ijaz Ahmad, Na Sun, Jianzhong Zhou, Xiaohua Dong

“…Data have been analyzed by comparing the simulation outputs of the models with a correlation coefficient (R), root mean square errors (RMSE), Nash-Sutcliffe Efficiency (NSE), mean absolute percentage error (MAPE), and mean absolute errors (MAE). …”
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Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. by Christine Tedijanto, Solomon Aragie, Zerihun Tadesse, Mahteme Haile, Taye Zeru, Scott D Nash, Dionna M Wittberg, Sarah Gwyn, Diana L Martin, Hugh J W Sturrock, Thomas M Lietman, Jeremy D Keenan, Benjamin F Arnold

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Seasonal predictability of summer north african subtropical high in operational climate prediction models by Fang Zhou, Ali Said Juma, Ran Zi, Jian Shi, Ming-Hong Liu

“…Seasonal predictability of summer North African Subtropical High (NASH) is investigated in this study by utilizing the hindcast data from four operational climate prediction models, including BCC_CSM1.1(m), NCEP CFSv2, ECMWF System 4, and JMA CPSv2. …”
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Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective. by Ali Canbay, Julia Kälsch, Ursula Neumann, Monika Rau, Simon Hohenester, Hideo A Baba, Christian Rust, Andreas Geier, Dominik Heider, Jan-Peter Sowa

“…BACKGROUND & AIMS:Current non-invasive scores for the assessment of severity of non-alcoholic fatty liver disease (NAFLD) and identification of patients with non-alcoholic steatohepatitis (NASH) have insufficient performance to be included in clinical routine. …”
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Hydrometeorological Insights into the Forecasting Performance of Multi-Source Weather over a Typical Hill-Karst Basin, Southwest China by Chongxun Mo, Xiaoyu Wan, Xingbi Lei, Xinru Chen, Rongyong Ma, Yi Huang, Guikai Sun

“…Furthermore, MSWX achieved satisfactory performance (Nash–Sutcliffe value > 0) in 22% of runoff event predictions, but the error increased with longer lead times. …”
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Improved Distributed Predictive Functional Control With Basic Function and PID Control Structure by Zhe Yu, Jianjun Bai, Hongbo Zou

“…Further, the coupling effect between subsystems can be eliminated by employing Nash game theory, then the improved DPFC approach is obtained. …”
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Application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting by Yi Ji, Hong-Tao Dong, Zhen-Xiang Xing, Ming-Xin Sun, Qiang Fu, Dong Liu

“…The forecasting results show that the Nash efficiency coefficient of the LSSVM model is 0.815, and the Nash efficiency coefficient of the CEEMD-LSSVM model is 0.954, an increase of 13.9%. …”
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Statistical summer mass-balance forecast model with application to Brúarjökull glacier, South East Iceland by DARRI EYTHORSSON, SIGURDUR M. GARDARSSON, ANDRI GUNNARSSON, BIRGIR HRAFNKELSSON

“…A mass-balance estimate was calculated as a uniform average across ensemble predictions. The method was evaluated using fivefold cross-validation and the statistical metrics Nash–Sutcliffe efficiency, the ratio of the root mean square error to the std dev. and percent bias. …”
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