In this paper, we present the experimental results of our investigation of network formation and the distribution of actions in a population of players whose members may select their partners in a bilateral 2x2 Hawk-Dove base game. In the population game, exploitive Hawk behavior leads to inefficiency while cooperative Dove behavior leads to efficient outcomes. The experiment was conducted in c...

<p style='text-indent:20px;'>We study a nonzero-sum risk-sensitive stochastic differential game for controlled reflecting diffusion processes in the nonnegative orthant. We treat two cost evaluation criteria, namely, discounted and ergodic cost. Under certain assumptions, we establish existence of Nash equilibria. Also, completely characterize equilibrium criterion space stationary Markov...

This paper presents a modeling approach based on Artificial Neural Networks (ANNs) in the Ouémé river catchment at Savè. To do this, we used precipitation data as input over period 1965 -2010 to simulate discharge study area by using two ANNs models such Long Short Term Memory (LSTM) and Recurrent Gate (GRU) models. Indeed, description of stochastic nature is better presented today than statist...

This study compares the performance of three different neural network models to estimate daily streamflow in a watershed under natural flow regime. Based on existing and public tools, types NN were developed, namely, multi-layer perceptron, long short-term memory, convolutional network. Precipitation was either considered an input variable its own or combined with air temperature as another var...

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, datasets require detailed validation to determine their accuracy reliability. This paper evaluates the performance MERRA2 ERA5 regarding monthly rainfall prod...

Precise estimation of water temperature plays a key role in environmental impact assessment, aquatic ecosystems’ management and resources planning management. In the current study, convolutional neural networks (CNN) long short-term memory (LSTM) network-based deep learning models were examined to estimate daily temperatures Bailong River China. Two novel optimization algorithms, namely reptile...

Abstract Various hydrological models were used in different river basins to simulate the runoff on available rainfall, land use and soil property data. The HEC-HMS model is by several researchers estimate water potential of basin through rainfall-runoff modeling. In this study, a for Punpun has been developed using HEC-HMS. Daily rainfall data from years 2005 2017 development model. ArcGIS anal...

Abstract The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization the ANN model's hyperparameters with a genetic algorithm (GA) and use growing window approach training model are also results compared to those commonly used time series models, namely Autoregressive Integrated Moving Average (ARIMA) pattern-based ...