# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 1 Introduction  
Sewater temperature is an important indicator that measures water heat and is, thus, one of the most important physical factors of the marine environment. The changes in seawater temperature depend on the process of solar radiation, heat exchange between atmosphere and seawater, evaporation, submarine earth activity, ocean internal radioactive fission and a biochemistry process [1]. Oceans cover three-quarters of the Earth and their specific heat capacity is rather high, with the ocean playing an important role in the regulation of the global climate. Ocean-temperature prediction makes humans better understand global climate change and marine ecosystems. There are many factors that can result in the change of water temperature, mainly divided into the influence of time and space [2]. Spatiality is embodied in different areas that have different seawater temperature. The farther the distance, the more obvious the temperature differences are. Timeliness is reflected in water temperature in different times, different quarters, and different years are various. Due to ocean temperature being affected by many factors, complex and changeable, carrying on an accurate prediction of space and time at the same time is a challenge [3]. Ocean-observing networks are essential for describing and understanding the ocean [4]. For the purpose of better observing the ocean and knowing more about the ocean, many large-scale global ocean-observation programs have been in progress, including the World's Oceans Real-Time Network Plan (ARGO), the Integrated Ocean Observation System (IOOS), the \"NEPTUNE\" seafloor observatory network planning (NEPTUNE), and European programs such as ESONET and DONET. These observed data have been used for numerical results comparison and also to increase knowledge on global oceanand climate [5]. Moreover, ocean observed data prediction depends on observed arrays and tools that are able to estimate forecasts [6]. In general, ocean forecasting methods are mainly divided into two categories: numerical-calculation and empirical-statistics methods. The numerical-calculation method is to establish a prediction model through kinetic and thermal equations, and make a numerical solution after given numerical conditions and boundary conditions [7]. From traditional empirical statistics methods to artificial intelligence approaches, there are many data-driven techniques for predicting ocean temperatures [8]. The representative statistical techniques include Markov model [9], empirical canonical correlation analysis [10]. However, in recent years, the neural network has been used successfully in many applications. In marine-temperature research, the neural network plays a very important role. In [11], [PERSON] et al. used neural-network models to seasonally forecast tropical Pacific sea-surface temperature anomalies (SSTA) in the Nino 3.4 region (6\({}^{\circ}\) S-6\({}^{\circ}\) N, 120\({}^{\circ}\) W-170\({}^{\circ}\) W). [PERSON] et al. predicted the SST over the tropical Pacific using a neural network (NN) that consisted of deriving the SST principal components over a three- to 15-month lead time using the input of SST and sea level pressure [12]. In [13], [PERSON] et al. analyzed the SST data in the Indian Ocean and used the ANN technique to study the predictability of the Indian summer monsoon. It has been found that the performance of the ANN model was better than the corresponding regression model in the prediction of Indian summer-monsoon rainfall. Marine Temperature has the characteristic of changing with time, which means that each point contains a large amount of data. Recurrent NNs (RNN), as an important form of neural network, is used to process sequence data. However, RNN suffers a lot from vanishing- or exploding-gradient problems that cannot solve the long-term dependence problem. As a special RNN, Long short-term memory (LSTM) introduces the gate mechanism and can prevent back-propagated errors from vanishing or exploding [14].